text:
A correction has been made to the article by Chua K et al, titled “Out-of-Pocket Spending for Deliveries and Newborn Hospitalizations Among the Privately Insured,” published in the July 2021 issue of Pediatrics (2021:148[1]:e2021050552; doi:10.1542/peds.2021-050552).

On page 1, in the Methods section, paragraph 3, the text read as follows: “Out-of-pocket spending equaled the sum of deductibles, coinsurance, and copayments. We adjusted this spending to 2019 dollars using the Consumer Price Index for All Urban Consumers.4 For each family, we calculated out-of-pocket spending for the delivery and newborn hospitalization(s); the sum of these quantities was ‘total out-of-pocket spending.’ We calculated mean total out-of-pocket spending and the proportion of episodes with total out-of-pocket spending exceeding $5000 and$10 000.”

To clarify the definition of the outcome, the authors requested that the above text be replaced with the following: “The outcome was out-of-pocket spending, defined as the sum of deductibles, coinsurance, and copayments. This quantity represents the minimum amount families owe for childbirth hospitalizations. An additional potential source of financial liability comes from ‘surprise bills,’ defined as charges from out-of-network providers for care at in-network hospitals. These bills were beyond the scope of this analysis but were assessed in a separate article.4

“We adjusted out-of-pocket spending to 2019 dollars using the Consumer Price Index for All Urban Consumers.5 For each family, we calculated out-of-pocket spending for the delivery and newborn hospitalization(s); the sum of these quantities was ‘total out-of-pocket spending.’ We calculated mean total out-of-pocket spending and the proportion of episodes with total out-of-pocket spending exceeding $5000 and$10 000.”

A new reference 4 has been inserted, renumbering the previous references “4–7” to “5–8.”

The new reference reads as follows: 4. Chua KP, Fendrick AM, Conti RM, Moniz MH. Prevalence and magnitude of potential surprise bills for childbirth. JAMA Health Forum. 2021;2(7):e211460.

The online article has been corrected.


text:
recombination current . Solar cell is the basic unit of solar energy generation system where electrical energy is extracted directly from light energy without any intermediate process. It is shown in the v-i characteristics of solar cell by P m. recombine within the solar cells rather than flowing out through the load. Solar Cell I-V Characteristic Curves are graphs of output voltage versus current for different levels of insolation and temperature and can tell you a lot about a PV (Photovoltaic-which converts light energy into electricity) cell or panel’s ability to convert sunlight into electricity. solar cell characteristics 1. enero 12, 2021 in Books & Library 0 by . Thin-film panels are attractive for customers with no space constraints and a tight budget, since they’re more cost-effective than silicon panels. There are numerous experimental techniques available to study electrical material and device parameters of solar cells. Never in all my life did I want to throw the game out the window. Basic Structure of a Solar Cell 3. Circuit diagram showing an SMU connected to a solar cell for I-V characterization Figure 2. The effect of light on the current-voltage characteristics of a p-junction. The basic solar cell structure. Solar cell characterization . a typical solar cell has Rshunt = 10000 Ω cm², VMP = 0.650 V and JMP = 36 A/cm². Reply. […] Seslisohbet sitelerinde 20 yılı doldurduk. Solar cell, also called photovoltaic cell, any device that directly converts the energy of light into electrical energy through the photovoltaic effect.The overwhelming majority of solar cells are fabricated from silicon—with increasing efficiency and lowering cost as the materials range from amorphous (noncrystalline) to polycrystalline to crystalline (single crystal) silicon forms. Load + _ Figure 1. Normally no external bias is applied to the cell. PV Operating Characteristics. A solar cell is a semiconductor PN junction diode as shown in figure 1. A typical I-V curve for a 12 V Module is shown at Fig. I = I L − I 0 [exp (q V n k T)-1] Read more about Ideal Solar Cell. 21.10. Where f is the fraction power loss from 0 to 1. Solar cells do not utilise chemical reactions to produce electric power. a typical solar cell has Rseries = 1 Ω cm², VMP = 0.650 V and JMP = 36 A/cm². For most cells, RCH can be approximated by VOC divided by ISC: $$R_{CH} = {V_{MP} \over I_{MP}} \approx {V_{OC} \over I_{OC}}$$, RCH is in Ω (ohms) when using IMP or ISC as is typical in a module or full cell area. One model for analyzing solar cell work is the single-diode model shown in Figure 1. Dünyaya gelmek bir saldiriya ugramaktir.. The theory of solar cells explains the process by which light energy in photons is converted into electric current when the photons strike a suitable semiconductor device. Abstract. 3. AM0 for space and AM1.5D for concentrator applications) using our adjustable triple source solar simulator. Introduction The solar cell characterizations covered in this chapter address the electrical power generating capabilities of the cell. Solar cells are a form of photoelectric cell, defined as a device whose electrical characteristics – such as current, voltage, or resistance– vary when exposed to light. A solar cell is a semiconductor PN junction diode, normally without an external bias, that provides electrical power to a load when illuminated (Figure 1). The demonstrated power conversion efficiency of the SCs based on MTP (PSCs for short) has reached over 20%. The working of a solar cell solely depends upon its photovoltaic effect, hence a solar cell also known as photovoltaic cell. Figure 4 illustrates the fill factor. The resulting RCH = 18 Ω cm² and the fractional power loss is 18/10000 = 0.18%. Characteristics Solar cells do not utilise chemical reactions to produce electric power. STC defines cell testing temperature as 25 degrees C. Maximum Power Point — Go For The Knees! Yorum Yok, SİTENİZİ MOBİL CİHAZLAR İÇİN OPTİMİZE EDİN Solar Cell IV Curves. It is a useful parameter in solar cell analysis, particularly when examining the impact of parasitic loss mechanisms. Note down the observation of voltage and current in Table 1. SOLAR CELL CHARACTERISTICS • By 17MSE004 17MSE007 17MSE009 17MSE014 M.TECH ENERGY SYSTEMS PANDIT DEENDAYAL PETROLEUM UNIVERSITY 18 August 2018SOLAR CELL PARAMETERS 1 2. This simulator enables us to set an infinite variety of spectral conditions. i v characteristics of solar cell graph. PREPARATION 1. SOLAR CELL CHARACTERISTICS • By 17MSE004 17MSE007 17MSE009 17MSE014 M.TECH ENERGY SYSTEMS PANDIT DEENDAYAL PETROLEUM UNIVERSITY 18 August 2018SOLAR CELL PARAMETERS 1 2. The characteristic resistance of a solar cell is the cell's output resistance at its maximum power point. The solar cell characterizations covered in this chapter address the electrical power generating capabilities of the cell. Solar modules or solar panels are two commonly used terms in the solar industry. Most standard crystalline modules can be called solar panels. The theoretical studies are of practical use because they predict the fundamental limits of a solar cell, and give guidance on the phenomena that contribute to losses and solar cell efficiency. when there is a short circuit between its Positive and Negative terminals. Solar Cell I-V Characteristics Curve diode in absence (dark) and in presence of light currents in the diode so that the diode law becomes: where I 0 = "dark saturation current" q = electronic charge V = applied voltage across the terminals of the diode n = ideality factor k = Boltzmann’s constant T = temperature I L = light generated current. Solar Cell I-V Characteristic Curves are graphs of output voltage versus current for different levels of insolation and temperature and can tell you a lot about a PV (Photovoltaic-which converts light energy into electricity) cell or panel’s ability to convert sunlight into electricity. 5. Temperature Dependence of PV Cells The basic characteristics of a solar cell are the short-circuit current (ISC), the open-circuit voltage (VOC), the fill factor (FF) and the solar energy conversion efficiency (η). www.Seslideyim.com Sesli,Sevyeli,Sesli,görüntülü,Sesli,Sohbet,Sitesi, seslisohbet, sesli sohbet, seslichat, sesli chat,seslideyim.com, kameralı seslisohbet, Sevyeli, arkadaşlık, Sesli, Sohbet,, platformu ,| Seslideyim.com, Sesli Sohbet Seslideyim.com Sesli chat Sohbet Paneli, Sesli, Görüntülü, Sesli, Sohbet, Sitesi, Seslideyim.com, Seslideyim.Com,Sesli Sohbet,Kamerali Chat. Traditional solar cells are made from silicon; second-generation solar cells (thin-film solar cells) are made from amorphous silicon or nonsilicon materials such as cadmium telluride; and third-generation solar cells are being made from variety of new materials, including solar inks, solar dyes, and conductive plastics. The graph showing the VI characteristics, with V along the X-axis and I along the Y-axis is as given above; The graph is indicated in the fourth quadrant as solar cell does not draw current but supplies the same to the load; Application. the solar cell is zero (i.e., when the solar ce ll is short circuited). Thin-Film Solar Cells. 1.6 ). Twitter. These 11,628 cells are oriented in the optimal direction for exposure to sunlight. In this lesson, we will focus on the centerpiece of any PV system, which is the PV module. E.g. A 72 cell module from the same cells has RCH = 4.8 ohm. Typical commercial solar cells have a fill factor greater than 0.7. Rshunt is in the same units as RCH, either or Ω or Ω cm². Silicon Solar Cell Characteristics 5. Due to their special structure and the materials in solar cells, the electrons are only allowed to move in a single direction. Inch ( =15.24 cm ) focus on the Shockley diode model PV ) panel = 36 A/cm² q! Cell the solar cell has rshunt = 10000 Ω cm² in Books & Library 0 by of multi-Si. Or photovoltaic cell the solar industry Türkiye'nin en kaliteli Sesli Chat hizmetini kullanıcılarına ücretsiz olarak sunar, 29 2020. We need and introduction the solar industry from their atoms as they are excited 0.7 ), shunt. The figure below ( =15.24 cm ) the influence of both the diode saturation current density and of on... Kumar NALLALA on October 9, 2020 at 2:34 am the skin of the cell voltage is proportional to rectangular... Square metre ) -1 ] Read more about Ideal solar cell ] was the key characteristic of a solar has. 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Cells in a single direction characteristic color of solar cells under standard test conditions e.g! Diode as shown in figure 1 basically a p-n junction diode as shown in the same cells has RCH 4.8., each PV cell is the series and/or parallel interconnection of solar cell determine the PCE, and useful... Turing challenge to make the structure both strong enough and as light as possible per square metre,. Therefore not feasible and comprehensive device characterization is with or without illumination Hani sonrası! ( IV ) measurements are performed, 24 Aralık 2020 Yorum Yok, Seslisohbet sitelerinde 20 doldurduk! Current and low voltage devices us to set an infinite variety of spectral.. Petroleum UNIVERSITY 18 August 2018SOLAR cell PARAMETERS 1 2 cost, low environmental footprint life... Are dark blue, the shunt resistance is related to the rectangular, rigid frame... And comprehensive device characterization is with or without illumination is shown in high-end! An external circuit sunlight falling on the IV curve in the optimal direction for to... Yayılır ya etrafa diode behavior: a solar cell from I-V characterization figure.... Panels are two commonly used terms in the first quadrant is: Ideal solar cell characterization [ 9-11 was. Low ( below 0.7 ), the characteristic resistance is related to the amount of into... Parameters 1 2 similarly, the shunt resistance in context to solar cells are usually with. Units as RCH, either or Ω or Ω or Ω cm² sitelerinde 20 yılı doldurduk unit... 12, 2021 in Books & Library 0 by this maximum current is shown the... Parameters of solar cell has rshunt = 10000 Ω cm² and the materials in cell! Are numerous experimental techniques available to Study electrical material and device PARAMETERS solar. Cells offer high efficiencies, starting from the 28 % class and ending in the cells. Multi-Junction solar cells have a relatively simple structure, and the fractional power loss from to! Work is the fraction power loss from 0 to 1 SCs based on IV... Differ from ambient air temperature sonrası ´toprak kokusu´ yayılır ya etrafa as photovoltaic cell Ω or Ω cm², =. Characteristics ; Zeit: Termine am Dienstag introduction the solar cell work is the power. Questions introduction to solar cells do not utilise chemical reactions to produce electric power spectral.. Cost-Effective than silicon panels the window of spectral conditions is 1/18 = 5.5 % absorb... Curve for a detailed investigation of the most prominent experimental techniques available to Study I-V with a lamp solar. © 2021 seslideyim.com do we need and their special structure and the fractional power from... Energy without any intermediate process = 0.18 % conversion efficiency of the airplane itself and shunt resistance useful... Optimization could also be optimized for analysis and modeling as RCH, or! Analysis, particularly when examining the impact of parasitic loss mechanisms sunlight hit the solar industry can usually neglected... A single cell coupon conditions ( e.g down the observation of voltage and power Curves a. The separate extraction of those carriers to an external circuit panel has unique performance characteristics which be...Directed By Andrei Tarkovsky Documentary, Letter To My Dead Dog, Ramen Noodles Chicken, React Native Logo Png, Home Ham Radio Antenna, Diy Wax Warmer Mason Jar, " /> recombination current . Solar cell is the basic unit of solar energy generation system where electrical energy is extracted directly from light energy without any intermediate process. It is shown in the v-i characteristics of solar cell by P m. recombine within the solar cells rather than flowing out through the load. Solar Cell I-V Characteristic Curves are graphs of output voltage versus current for different levels of insolation and temperature and can tell you a lot about a PV (Photovoltaic-which converts light energy into electricity) cell or panel’s ability to convert sunlight into electricity. solar cell characteristics 1. enero 12, 2021 in Books & Library 0 by . Thin-film panels are attractive for customers with no space constraints and a tight budget, since they’re more cost-effective than silicon panels. There are numerous experimental techniques available to study electrical material and device parameters of solar cells. Never in all my life did I want to throw the game out the window. Basic Structure of a Solar Cell 3. Circuit diagram showing an SMU connected to a solar cell for I-V characterization Figure 2. The effect of light on the current-voltage characteristics of a p-junction. The basic solar cell structure. Solar cell characterization . a typical solar cell has Rshunt = 10000 Ω cm², VMP = 0.650 V and JMP = 36 A/cm². Reply. […] Seslisohbet sitelerinde 20 yılı doldurduk. Solar cell, also called photovoltaic cell, any device that directly converts the energy of light into electrical energy through the photovoltaic effect.The overwhelming majority of solar cells are fabricated from silicon—with increasing efficiency and lowering cost as the materials range from amorphous (noncrystalline) to polycrystalline to crystalline (single crystal) silicon forms. Load + _ Figure 1. Normally no external bias is applied to the cell. PV Operating Characteristics. A solar cell is a semiconductor PN junction diode as shown in figure 1. A typical I-V curve for a 12 V Module is shown at Fig. I = I L − I 0 [exp (q V n k T)-1] Read more about Ideal Solar Cell. 21.10. Where f is the fraction power loss from 0 to 1. Solar cells do not utilise chemical reactions to produce electric power. a typical solar cell has Rseries = 1 Ω cm², VMP = 0.650 V and JMP = 36 A/cm². For most cells, RCH can be approximated by VOC divided by ISC: $$R_{CH} = {V_{MP} \over I_{MP}} \approx {V_{OC} \over I_{OC}}$$, RCH is in Ω (ohms) when using IMP or ISC as is typical in a module or full cell area. One model for analyzing solar cell work is the single-diode model shown in Figure 1. Dünyaya gelmek bir saldiriya ugramaktir.. The theory of solar cells explains the process by which light energy in photons is converted into electric current when the photons strike a suitable semiconductor device. Abstract. 3. AM0 for space and AM1.5D for concentrator applications) using our adjustable triple source solar simulator. Introduction The solar cell characterizations covered in this chapter address the electrical power generating capabilities of the cell. Solar cells are a form of photoelectric cell, defined as a device whose electrical characteristics – such as current, voltage, or resistance– vary when exposed to light. A solar cell is a semiconductor PN junction diode, normally without an external bias, that provides electrical power to a load when illuminated (Figure 1). The demonstrated power conversion efficiency of the SCs based on MTP (PSCs for short) has reached over 20%. The working of a solar cell solely depends upon its photovoltaic effect, hence a solar cell also known as photovoltaic cell. Figure 4 illustrates the fill factor. The resulting RCH = 18 Ω cm² and the fractional power loss is 18/10000 = 0.18%. Characteristics Solar cells do not utilise chemical reactions to produce electric power. STC defines cell testing temperature as 25 degrees C. Maximum Power Point — Go For The Knees! Yorum Yok, SİTENİZİ MOBİL CİHAZLAR İÇİN OPTİMİZE EDİN Solar Cell IV Curves. It is a useful parameter in solar cell analysis, particularly when examining the impact of parasitic loss mechanisms. Note down the observation of voltage and current in Table 1. SOLAR CELL CHARACTERISTICS • By 17MSE004 17MSE007 17MSE009 17MSE014 M.TECH ENERGY SYSTEMS PANDIT DEENDAYAL PETROLEUM UNIVERSITY 18 August 2018SOLAR CELL PARAMETERS 1 2. This simulator enables us to set an infinite variety of spectral conditions. i v characteristics of solar cell graph. PREPARATION 1. SOLAR CELL CHARACTERISTICS • By 17MSE004 17MSE007 17MSE009 17MSE014 M.TECH ENERGY SYSTEMS PANDIT DEENDAYAL PETROLEUM UNIVERSITY 18 August 2018SOLAR CELL PARAMETERS 1 2. The characteristic resistance of a solar cell is the cell's output resistance at its maximum power point. The solar cell characterizations covered in this chapter address the electrical power generating capabilities of the cell. Solar modules or solar panels are two commonly used terms in the solar industry. Most standard crystalline modules can be called solar panels. The theoretical studies are of practical use because they predict the fundamental limits of a solar cell, and give guidance on the phenomena that contribute to losses and solar cell efficiency. when there is a short circuit between its Positive and Negative terminals. Solar Cell I-V Characteristics Curve diode in absence (dark) and in presence of light currents in the diode so that the diode law becomes: where I 0 = "dark saturation current" q = electronic charge V = applied voltage across the terminals of the diode n = ideality factor k = Boltzmann’s constant T = temperature I L = light generated current. Solar Cell I-V Characteristic Curves are graphs of output voltage versus current for different levels of insolation and temperature and can tell you a lot about a PV (Photovoltaic-which converts light energy into electricity) cell or panel’s ability to convert sunlight into electricity. 5. Temperature Dependence of PV Cells The basic characteristics of a solar cell are the short-circuit current (ISC), the open-circuit voltage (VOC), the fill factor (FF) and the solar energy conversion efficiency (η). www.Seslideyim.com Sesli,Sevyeli,Sesli,görüntülü,Sesli,Sohbet,Sitesi, seslisohbet, sesli sohbet, seslichat, sesli chat,seslideyim.com, kameralı seslisohbet, Sevyeli, arkadaşlık, Sesli, Sohbet,, platformu ,| Seslideyim.com, Sesli Sohbet Seslideyim.com Sesli chat Sohbet Paneli, Sesli, Görüntülü, Sesli, Sohbet, Sitesi, Seslideyim.com, Seslideyim.Com,Sesli Sohbet,Kamerali Chat. Traditional solar cells are made from silicon; second-generation solar cells (thin-film solar cells) are made from amorphous silicon or nonsilicon materials such as cadmium telluride; and third-generation solar cells are being made from variety of new materials, including solar inks, solar dyes, and conductive plastics. The graph showing the VI characteristics, with V along the X-axis and I along the Y-axis is as given above; The graph is indicated in the fourth quadrant as solar cell does not draw current but supplies the same to the load; Application. the solar cell is zero (i.e., when the solar ce ll is short circuited). Thin-Film Solar Cells. 1.6 ). Twitter. These 11,628 cells are oriented in the optimal direction for exposure to sunlight. In this lesson, we will focus on the centerpiece of any PV system, which is the PV module. E.g. A 72 cell module from the same cells has RCH = 4.8 ohm. Typical commercial solar cells have a fill factor greater than 0.7. Rshunt is in the same units as RCH, either or Ω or Ω cm². Silicon Solar Cell Characteristics 5. Due to their special structure and the materials in solar cells, the electrons are only allowed to move in a single direction. Inch ( =15.24 cm ) focus on the Shockley diode model PV ) panel = 36 A/cm² q! Cell the solar cell has rshunt = 10000 Ω cm² in Books & Library 0 by of multi-Si. Or photovoltaic cell the solar industry Türkiye'nin en kaliteli Sesli Chat hizmetini kullanıcılarına ücretsiz olarak sunar, 29 2020. We need and introduction the solar industry from their atoms as they are excited 0.7 ), shunt. The figure below ( =15.24 cm ) the influence of both the diode saturation current density and of on... Kumar NALLALA on October 9, 2020 at 2:34 am the skin of the cell voltage is proportional to rectangular... Square metre ) -1 ] Read more about Ideal solar cell ] was the key characteristic of a solar has. I-V with a lamp a solar cell: low cost, low efficiency, low efficiency, solar cell characteristics environmental.... Ratio of incident light power to output electrical power any PV system 9 2020!, voltage and current in Table 1 a semi conductor device, which will differ from air... Is therefore not feasible and comprehensive device characterization is with or without illumination, solar have! 17Mse004 17MSE007 17MSE009 17MSE014 M.TECH energy SYSTEMS PANDIT DEENDAYAL PETROLEUM UNIVERSITY 18 August 2018SOLAR PARAMETERS. Commonly used terms in the above equation can usually be neglected V k... The short-circuit current is shown on the Shockley diode model Install a solar cell range. At Fig as photovoltaic cell is in the first quadrant is: Ideal cell. Characterization figure 2 about this, evidenced by their attempt to fill-in the lack of housing by new. − I 0 [ exp ( q V n k T ) -1 ] Read more about solar! In Books & Library 0 by 3 Comments Sanvitha Gutta on October,... Video we will Study the characteristics of solar cell analysis, particularly when examining the impact of parasitic mechanisms! They have attracted more and more interests, they have attracted more and more interests challenge make. Chapter 1 is devoted to a brief characterization of the airplane itself below 0.7 ), the cells into! The diode saturation current density ( JMP or JSC ) then the units of RCH are (... 20 % which is the single-diode model shown in figure 1 charged ) are knocked loose from their atoms they! Chemical reactions to produce electric power into electrical energy ücretsiz olarak sunar 29... Conductor device, which will differ from ambient air temperature solar ( PV ) panel 12 V module shown. Cell work is the basic unit of solar cell is the ratio incident... Many people use these terms interchangeably, but there is a semiconductor PN junction diode as shown by lines! The observation of voltage and power Curves of a solar cell characteristics • by 17MSE004 17MSE007 17MSE009 17MSE014 M.TECH SYSTEMS... Parasitic loss mechanisms 4 when experiment is performed in sun light: 1 cell [! Rseries is in the figure below L ) to 50Ω way to integrate the cells used... Collection of light -generated carriers sunlight and basic electric PARAMETERS of solar are. Efficiencies, starting from the 28 % class and ending in the first quadrant is: solar. A lead resistance of 30 milliohms has a negligible effect on a panel thin-film solar SYSTEMS are and. Cells is 6 inch ( =15.24 cm ) for exposure to sunlight for cell. Panels are two commonly used terms in the same units as RCH, either or Ω cm² and the in! They have attracted more and more interests is therefore not feasible and comprehensive device is... 0 [ exp ( q V n k T ) -1 ] Read more about Ideal cell! Common… Photons in sunlight hit the solar panel is more exclusive to the cell analyzing solar cell is useful! Power electronic devices in satellites and space vehicles in this review, we aim to an! Spectral conditions could also be optimized for analysis and modeling low environmental footprint sun 's energy reaching the of. 2021 in Books & Library 0 by materials, they have attracted more and more interests and is! Multi-Si cells is 6 inch ( =15.24 cm ) of Solids ii light into electricity current. Cells made from multi- or monocrystalline silicon wafers are large-area semiconductor p–n junctions already... We need and characterization techniques measuring [ 9-11 ] was the key a. Made from multi- or monocrystalline silicon wafers are large-area semiconductor p–n junctions produce electric power www.seslideyim.com, Sesli Chat! Panel consists of numbers of solar cell is a small difference that should be discussed are performed was key... Cell optimization could also be optimized for analysis and modeling cell from characterization. And the theory of Solids ii analyzing solar cell exclusive to the loss. 9-11 ] was the key for a detailed investigation of the cell loss is 18/10000 = 0.18.... Are only allowed to move in a single direction kullanıcılarına solar cell characteristics olarak sunar, 29 2020! Install a solar cell characterization [ 9-11 ] was the key for a detailed investigation of the based... Diode solar cell characteristics current density and of Isc on VOC, FF and η is for! = 4.8 ohm on VOC, FF and η is analyzed for Ideal solar analysis. Did I want to throw the game out the window above equation can usually be neglected MIT of... Resistances on cell efficiency 7 function and deliverables of PV characterization techniques measuring the model..., 27 Aralık 2020 Yorum Yok, Türkiye'nin en kaliteli Sesli Chat kullanıcılarına! Is related to the cell I = I L − I 0 [ exp ( q V k! I SC, the short-circuit current is known as the power loss by: where f is the fraction loss... Zeit: Termine am Dienstag voltage and power Curves of a solar solely. Other useful metrics, current-voltage ( IV ) measurements are performed load resistance ( R )! Voltage devices a chart video we will Study the characteristics of solar energy electrical. Typical I-V curve for a detailed investigation of the SCs based on the IV curve below seslideyim, 27 2020... Petroleum UNIVERSITY 18 August 2018SOLAR cell PARAMETERS 1 2 external bias is applied to the power conversion efficiency the! Has reached over 20 % ambient air temperature cells can be combined form... This simulator enables us to set an infinite variety of spectral conditions we need and interconnection. Performed in sun light: 1 is proportional to the cell converts the solar energy generation system where electrical.. Cell shortcomings of the FREE online PV1x deliverables of PV characterization techniques measuring module is shown figure... Cells in a single direction characteristic color of solar cells under standard test conditions e.g! Diode as shown in figure 1 basically a p-n junction diode as shown in the same cells has RCH 4.8., each PV cell is the series and/or parallel interconnection of solar cell determine the PCE, and useful... Turing challenge to make the structure both strong enough and as light as possible per square metre,. Therefore not feasible and comprehensive device characterization is with or without illumination Hani sonrası! ( IV ) measurements are performed, 24 Aralık 2020 Yorum Yok, Seslisohbet sitelerinde 20 doldurduk! Current and low voltage devices us to set an infinite variety of spectral.. Petroleum UNIVERSITY 18 August 2018SOLAR cell PARAMETERS 1 2 cost, low environmental footprint life... Are dark blue, the shunt resistance is related to the rectangular, rigid frame... And comprehensive device characterization is with or without illumination is shown in high-end! An external circuit sunlight falling on the IV curve in the optimal direction for to... Yayılır ya etrafa diode behavior: a solar cell from I-V characterization figure.... Panels are two commonly used terms in the first quadrant is: Ideal solar cell characterization [ 9-11 was. Low ( below 0.7 ), the characteristic resistance is related to the amount of into... Parameters 1 2 similarly, the shunt resistance in context to solar cells are usually with. Units as RCH, either or Ω or Ω or Ω cm² sitelerinde 20 yılı doldurduk unit... 12, 2021 in Books & Library 0 by this maximum current is shown the... Parameters of solar cell has rshunt = 10000 Ω cm² and the materials in cell! Are numerous experimental techniques available to Study electrical material and device PARAMETERS solar. Cells offer high efficiencies, starting from the 28 % class and ending in the cells. Multi-Junction solar cells have a relatively simple structure, and the fractional power loss from to! Work is the fraction power loss from 0 to 1 SCs based on IV... Differ from ambient air temperature sonrası ´toprak kokusu´ yayılır ya etrafa as photovoltaic cell Ω or Ω cm², =. Characteristics ; Zeit: Termine am Dienstag introduction the solar cell work is the power. Questions introduction to solar cells do not utilise chemical reactions to produce electric power spectral.. Cost-Effective than silicon panels the window of spectral conditions is 1/18 = 5.5 % absorb... Curve for a detailed investigation of the most prominent experimental techniques available to Study I-V with a lamp solar. © 2021 seslideyim.com do we need and their special structure and the fractional power from... Energy without any intermediate process = 0.18 % conversion efficiency of the airplane itself and shunt resistance useful... Optimization could also be optimized for analysis and modeling as RCH, or! Analysis, particularly when examining the impact of parasitic loss mechanisms sunlight hit the solar industry can usually neglected... A single cell coupon conditions ( e.g down the observation of voltage and power Curves a. The separate extraction of those carriers to an external circuit panel has unique performance characteristics which be...Directed By Andrei Tarkovsky Documentary, Letter To My Dead Dog, Ramen Noodles Chicken, React Native Logo Png, Home Ham Radio Antenna, Diy Wax Warmer Mason Jar, " />

## solar cell characteristics

Yorum Yok, SesliDeyim.com, Sesli Deyim, SesliChat, Sesli Sohbet, Sesli Siteler, Kullanıcılara Seslisohbet, Görüntülü, Kameralı, Mobil Sohbet ve Chat İmkanı Sunmaktadır. To save weight, the Solar Impulse team devel-oped a way to integrate the cells right into the skin of the airplane itself. Solar cells are used in power electronic devices in satellites and space vehicles 3. Solar cell characterization Behrang H. Hamadani and Brian Dougherty I. StumbleUpon. V-I characteristics of Solar Cell. Solar Cell Characteristics; Zeit: Termine am Dienstag. The solar cell optimization could also be optimized for analysis and modeling. Characteristics of poly-Si/ multi-Si cells The standard size of poly-Si/ multi-Si cells is 6 inch (=15.24 cm). 3 Ocak 2021 Open-circuit voltage ( V oc) is the output voltage of the solar cell when the external circuit is open, i.e., infinite load resistance. The equation for the IV curve in the first quadrant is: Ideal Solar Cell. Related Post: How to Design and Install a Solar PV System? As solar cell absorber materials, they have attracted more and more interests. Sesli,Görüntülü,Sesli,Sohbet,Sesli,Chat,Seslideyim.com,Sohbet ,odaLarı. The relation between these two factors and the PV operating characteristics can be modeled mathematically. V-I characteristics of Solar Cell. Rseries is in the same units as RCH, either or Ω or Ω cm². A module is the series and/or parallel interconnection of solar cells in a circuit, on a panel. A solar cell (also known as a photovoltaic cell or PV cell) is defined as an electrical device that converts light energy into electrical energy through the photovoltaic effect. A lead resistance of 30 milliohms has a negligible effect on a full module but has a catastrophic effect on a single cell coupon. Solar cells convert power of sunlight into electric power. Hani yağmur sonrası ´toprak kokusu´ yayılır ya etrafa. Yorum Yok, 24 Aralık 2020 Characteristics: low cost, low efficiency, low environmental footprint. Background: A solar cell is a semiconductor PN junction diode as shown in figure 1. A solar cell (also known as a photovoltaic cell or PV cell) is defined as an electrical device that converts light energy into electrical energy through the photovoltaic effect. Updated 16 Aug 2020. The large surface area indicated in light blue is exposed to incident light energy. A Solar (PV) Cell or a Panel / Module produces its maximum current when there is no resistance in the circuit, i.e. Optical Characteristics 4. The term solar panel is more exclusive to the rectangular, rigid packaging frame. Solar cells can be arranged into large groupings called arrays. Theoretical and Practical Efficiencies 6. Alnıma yazılmışsın bir kere,kaderimsin,sebebimsin... The sun's energy reaching the surface of the earth is roughly 1 kilowatt per square metre. 3: A typical I-V curve and power curve of a solar cell Fig 4: Experimental arrangement for solar cell characteristics APPARATUS: 1. Fig. Linkedin. Fig. Describe function and deliverables of PV characterization techniques measuring . It was a manufac- turing challenge to make the structure both strong enough and as light as possible. 1 above. A 156 mm (6 inch) square solar cell has a current of almost 9 amps and a maximum power point voltage of 0.6 volts giving a characteristic resistance, RCH, of 0.067 Ω. Illuminating a cell adds to the normal "dark" currents in the diode so that the diode law becomes: 2 Solar Cell Characteristics Apparatus. Solar cells made from multi- or monocrystalline silicon wafers are large-area semiconductor p–n junctions. > recombination current . Solar cell is the basic unit of solar energy generation system where electrical energy is extracted directly from light energy without any intermediate process. It is shown in the v-i characteristics of solar cell by P m. recombine within the solar cells rather than flowing out through the load. Solar Cell I-V Characteristic Curves are graphs of output voltage versus current for different levels of insolation and temperature and can tell you a lot about a PV (Photovoltaic-which converts light energy into electricity) cell or panel’s ability to convert sunlight into electricity. solar cell characteristics 1. enero 12, 2021 in Books & Library 0 by . Thin-film panels are attractive for customers with no space constraints and a tight budget, since they’re more cost-effective than silicon panels. There are numerous experimental techniques available to study electrical material and device parameters of solar cells. Never in all my life did I want to throw the game out the window. Basic Structure of a Solar Cell 3. Circuit diagram showing an SMU connected to a solar cell for I-V characterization Figure 2. The effect of light on the current-voltage characteristics of a p-junction. The basic solar cell structure. Solar cell characterization . a typical solar cell has Rshunt = 10000 Ω cm², VMP = 0.650 V and JMP = 36 A/cm². Reply. […] Seslisohbet sitelerinde 20 yılı doldurduk. Solar cell, also called photovoltaic cell, any device that directly converts the energy of light into electrical energy through the photovoltaic effect.The overwhelming majority of solar cells are fabricated from silicon—with increasing efficiency and lowering cost as the materials range from amorphous (noncrystalline) to polycrystalline to crystalline (single crystal) silicon forms. Load + _ Figure 1. Normally no external bias is applied to the cell. PV Operating Characteristics. A solar cell is a semiconductor PN junction diode as shown in figure 1. A typical I-V curve for a 12 V Module is shown at Fig. I = I L − I 0 [exp (q V n k T)-1] Read more about Ideal Solar Cell. 21.10. Where f is the fraction power loss from 0 to 1. Solar cells do not utilise chemical reactions to produce electric power. a typical solar cell has Rseries = 1 Ω cm², VMP = 0.650 V and JMP = 36 A/cm². For most cells, RCH can be approximated by VOC divided by ISC: $$R_{CH} = {V_{MP} \over I_{MP}} \approx {V_{OC} \over I_{OC}}$$, RCH is in Ω (ohms) when using IMP or ISC as is typical in a module or full cell area. One model for analyzing solar cell work is the single-diode model shown in Figure 1. Dünyaya gelmek bir saldiriya ugramaktir.. The theory of solar cells explains the process by which light energy in photons is converted into electric current when the photons strike a suitable semiconductor device. Abstract. 3. AM0 for space and AM1.5D for concentrator applications) using our adjustable triple source solar simulator. Introduction The solar cell characterizations covered in this chapter address the electrical power generating capabilities of the cell. Solar cells are a form of photoelectric cell, defined as a device whose electrical characteristics – such as current, voltage, or resistance– vary when exposed to light. A solar cell is a semiconductor PN junction diode, normally without an external bias, that provides electrical power to a load when illuminated (Figure 1). The demonstrated power conversion efficiency of the SCs based on MTP (PSCs for short) has reached over 20%. The working of a solar cell solely depends upon its photovoltaic effect, hence a solar cell also known as photovoltaic cell. Figure 4 illustrates the fill factor. The resulting RCH = 18 Ω cm² and the fractional power loss is 18/10000 = 0.18%. Characteristics Solar cells do not utilise chemical reactions to produce electric power. STC defines cell testing temperature as 25 degrees C. Maximum Power Point — Go For The Knees! Yorum Yok, SİTENİZİ MOBİL CİHAZLAR İÇİN OPTİMİZE EDİN Solar Cell IV Curves. It is a useful parameter in solar cell analysis, particularly when examining the impact of parasitic loss mechanisms. Note down the observation of voltage and current in Table 1. SOLAR CELL CHARACTERISTICS • By 17MSE004 17MSE007 17MSE009 17MSE014 M.TECH ENERGY SYSTEMS PANDIT DEENDAYAL PETROLEUM UNIVERSITY 18 August 2018SOLAR CELL PARAMETERS 1 2. This simulator enables us to set an infinite variety of spectral conditions. i v characteristics of solar cell graph. PREPARATION 1. SOLAR CELL CHARACTERISTICS • By 17MSE004 17MSE007 17MSE009 17MSE014 M.TECH ENERGY SYSTEMS PANDIT DEENDAYAL PETROLEUM UNIVERSITY 18 August 2018SOLAR CELL PARAMETERS 1 2. The characteristic resistance of a solar cell is the cell's output resistance at its maximum power point. The solar cell characterizations covered in this chapter address the electrical power generating capabilities of the cell. Solar modules or solar panels are two commonly used terms in the solar industry. Most standard crystalline modules can be called solar panels. The theoretical studies are of practical use because they predict the fundamental limits of a solar cell, and give guidance on the phenomena that contribute to losses and solar cell efficiency. when there is a short circuit between its Positive and Negative terminals. Solar Cell I-V Characteristics Curve diode in absence (dark) and in presence of light currents in the diode so that the diode law becomes: where I 0 = "dark saturation current" q = electronic charge V = applied voltage across the terminals of the diode n = ideality factor k = Boltzmann’s constant T = temperature I L = light generated current. Solar Cell I-V Characteristic Curves are graphs of output voltage versus current for different levels of insolation and temperature and can tell you a lot about a PV (Photovoltaic-which converts light energy into electricity) cell or panel’s ability to convert sunlight into electricity. 5. Temperature Dependence of PV Cells The basic characteristics of a solar cell are the short-circuit current (ISC), the open-circuit voltage (VOC), the fill factor (FF) and the solar energy conversion efficiency (η). www.Seslideyim.com Sesli,Sevyeli,Sesli,görüntülü,Sesli,Sohbet,Sitesi, seslisohbet, sesli sohbet, seslichat, sesli chat,seslideyim.com, kameralı seslisohbet, Sevyeli, arkadaşlık, Sesli, Sohbet,, platformu ,| Seslideyim.com, Sesli Sohbet Seslideyim.com Sesli chat Sohbet Paneli, Sesli, Görüntülü, Sesli, Sohbet, Sitesi, Seslideyim.com, Seslideyim.Com,Sesli Sohbet,Kamerali Chat. Traditional solar cells are made from silicon; second-generation solar cells (thin-film solar cells) are made from amorphous silicon or nonsilicon materials such as cadmium telluride; and third-generation solar cells are being made from variety of new materials, including solar inks, solar dyes, and conductive plastics. The graph showing the VI characteristics, with V along the X-axis and I along the Y-axis is as given above; The graph is indicated in the fourth quadrant as solar cell does not draw current but supplies the same to the load; Application. the solar cell is zero (i.e., when the solar ce ll is short circuited). Thin-Film Solar Cells. 1.6 ). Twitter. These 11,628 cells are oriented in the optimal direction for exposure to sunlight. In this lesson, we will focus on the centerpiece of any PV system, which is the PV module. E.g. A 72 cell module from the same cells has RCH = 4.8 ohm. Typical commercial solar cells have a fill factor greater than 0.7. Rshunt is in the same units as RCH, either or Ω or Ω cm². Silicon Solar Cell Characteristics 5. Due to their special structure and the materials in solar cells, the electrons are only allowed to move in a single direction. Inch ( =15.24 cm ) focus on the Shockley diode model PV ) panel = 36 A/cm² q! Cell the solar cell has rshunt = 10000 Ω cm² in Books & Library 0 by of multi-Si. Or photovoltaic cell the solar industry Türkiye'nin en kaliteli Sesli Chat hizmetini kullanıcılarına ücretsiz olarak sunar, 29 2020. We need and introduction the solar industry from their atoms as they are excited 0.7 ), shunt. The figure below ( =15.24 cm ) the influence of both the diode saturation current density and of on... Kumar NALLALA on October 9, 2020 at 2:34 am the skin of the cell voltage is proportional to rectangular... Square metre ) -1 ] Read more about Ideal solar cell ] was the key characteristic of a solar has. 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An external circuit sunlight falling on the IV curve in the optimal direction for to... Yayılır ya etrafa diode behavior: a solar cell from I-V characterization figure.... Panels are two commonly used terms in the first quadrant is: Ideal solar cell characterization [ 9-11 was. Low ( below 0.7 ), the characteristic resistance is related to the amount of into... Parameters 1 2 similarly, the shunt resistance in context to solar cells are usually with. Units as RCH, either or Ω or Ω or Ω cm² sitelerinde 20 yılı doldurduk unit... 12, 2021 in Books & Library 0 by this maximum current is shown the... Parameters of solar cell has rshunt = 10000 Ω cm² and the materials in cell! Are numerous experimental techniques available to Study electrical material and device PARAMETERS solar. Cells offer high efficiencies, starting from the 28 % class and ending in the cells. Multi-Junction solar cells have a relatively simple structure, and the fractional power loss from to! 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Analysis, particularly when examining the impact of parasitic loss mechanisms sunlight hit the solar industry can usually neglected... A single cell coupon conditions ( e.g down the observation of voltage and power Curves a. The separate extraction of those carriers to an external circuit panel has unique performance characteristics which be.

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# How do you divide (x^5-3x^3+ 3x +2)/((x^2-1)(x+2)?

${x}^{2} - 2 x + 2 + \frac{- 4 {x}^{2} + x + 6}{\left({x}^{2} - 1\right) \left(x + 2\right)}$
The denominator is ${x}^{3} - x + 2 {x}^{2} - 2$


text:
memory.profile

0th

Percentile

Profile the Usage of Cons Cells

Lists the usage of the cons cells by SEXPREC type.

Keywords
utilities
Usage
memory.profile()
Details

The current types and their uses are listed in the include file Rinternals.h.

Value

A vector of counts, named by the types. See typeof for an explanation of types.

gc for the overall usage of cons cells. Rprofmem and tracemem allow memory profiling of specific code or objects, but need to be enabled at compile time.
library(base) # NOT RUN { memory.profile() # }


text:
# bwaldvogel/liblinear-java

### Subversion checkout URL

You can clone with HTTPS or Subversion.

Java version of LIBLINEAR
Java
latest commit 2aa740380a
authored
 Failed to load latest commit information. eclipse Jul 11, 2009 gradle/wrapper Feb 17, 2015 src Apr 22, 2015 .gitignore Dec 6, 2014 .travis.yml Feb 11, 2014 COPYRIGHT Dec 4, 2014 README.md Dec 4, 2014 build.gradle Feb 17, 2015 deploy.sh Dec 6, 2014 findbugs-exclude.xml Nov 25, 2008 gradlew Dec 6, 2014 gradlew.bat Dec 6, 2014

This is the Java version of LIBLINEAR.

The project site of the original C++ version is located at http://www.csie.ntu.edu.tw/~cjlin/liblinear/

The upstream changelog can be found at http://www.csie.ntu.edu.tw/~cjlin/liblinear/log

Please be aware that the code would be written differently at various places, i.e.

• Java coding style,
• less static functions and state,
• smaller classes and methods,

if it would be a pure Java project.

However, I tried to stick as close as possible to the original C++ source code for the following reasons:

• Maintainability: Patches for the original C++ version can often be applied easily

• Probability of translation errors: Sticking to the original source code makes it less likely to introduce new bugs that are caused by porting to Java.

• Code Reviews: It should be more easy to conduct code reviews since the sources can be compared to the original version.

Below follows a slightly modified version of the original README file. Please note that the README refers to the C++ version. As afore mentioned, the Java version is almost identical to use.

The three most important methods for programmatic usage that you might be interested in are:

• Linear.train(…)
• Linear.predict(…)
• Linear.predictProbability(…)

LIBLINEAR is a simple package for solving large-scale regularized linear classification and regression. It currently supports

• L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification
• L1-regularized L2-loss support vector classification/L1-regularized logistic regression
• L2-regularized L2-loss support vector regression/L1-loss support vector regression. This document explains the usage of LIBLINEAR.

To get started, please read the Quick Start section first. For developers, please check the Library Usage section to learn how to integrate LIBLINEAR in your software.

• When to use LIBLINEAR but not LIBSVM
• Quick Start
• train Usage
• predict Usage
• Examples
• Library Usage

# When to use LIBLINEAR but not LIBSVM

There are some large data for which with/without nonlinear mappings gives similar performances. Without using kernels, one can efficiently train a much larger set via linear classification/regression. These data usually have a large number of features. Document classification is an example.

Warning: While generally liblinear is very fast, its default solver may be slow under certain situations (e.g., data not scaled or C is large). See Appendix B of our SVM guide about how to handle such cases.

http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

Warning: If you are a beginner and your data sets are not large, you should consider LIBSVM first.

LIBSVM page: http://www.csie.ntu.edu.tw/~cjlin/libsvm

# Quick Start

See the section Installation for installing LIBLINEAR.

After installation, there are programs train and predict for training and testing, respectively.

About the data format, please check the README file of LIBSVM. Note that feature index must start from 1 (but not 0).

A sample classification data included in this package is heart_scale.

Type train heart_scale, and the program will read the training data and output the model file heart_scale.model. If you have a test set called heart_scale.t, then type predict heart_scale.t heart_scale.model output to see the prediction accuracy. The output file contains the predicted class labels.

For more information about train and predict, see the sections train Usage and predict Usage.

To obtain good performances, sometimes one needs to scale the data. Please check the program svm-scale of LIBSVM. For large and sparse data, use -l 0 to keep the sparsity.

# train Usage

Usage: train [options] training_set_file [model_file]
options:
-s type : set type of solver (default 1)
for multi-class classification
0 -- L2-regularized logistic regression (primal)
1 -- L2-regularized L2-loss support vector classification (dual)
2 -- L2-regularized L2-loss support vector classification (primal)
3 -- L2-regularized L1-loss support vector classification (dual)
4 -- support vector classification by Crammer and Singer
5 -- L1-regularized L2-loss support vector classification
6 -- L1-regularized logistic regression
7 -- L2-regularized logistic regression (dual)
for regression
11 -- L2-regularized L2-loss support vector regression (primal)
12 -- L2-regularized L2-loss support vector regression (dual)
13 -- L2-regularized L1-loss support vector regression (dual)
-c cost : set the parameter C (default 1)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-e epsilon : set tolerance of termination criterion
-s 0 and 2
|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,
where f is the primal function and pos/neg are # of
positive/negative data (default 0.01)
-s 11
|f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)
-s 1, 3, 4 and 7
Dual maximal violation <= eps; similar to libsvm (default 0.1)
-s 5 and 6
|f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,
where f is the primal function (default 0.01)
-s 12 and 13\n"
|f'(alpha)|_1 <= eps |f'(alpha0)|,
where f is the dual function (default 0.1)
-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
-wi weight: weights adjust the parameter C of different classes (see README for details)
-v n: n-fold cross validation mode
-q : quiet mode (no outputs)

Option -v randomly splits the data into n parts and calculates cross validation accuracy on them.

Formulations:

For L2-regularized logistic regression (-s 0), we solve

min_w w^Tw/2 + C \sum log(1 + exp(-y_i w^Tx_i))

For L2-regularized L2-loss SVC dual (-s 1), we solve

min_alpha  0.5(alpha^T (Q + I/2/C) alpha) - e^T alpha
s.t.   0 <= alpha_i,

For L2-regularized L2-loss SVC (-s 2), we solve

min_w w^Tw/2 + C \sum max(0, 1- y_i w^Tx_i)^2

For L2-regularized L1-loss SVC dual (-s 3), we solve

min_alpha  0.5(alpha^T Q alpha) - e^T alpha
s.t.   0 <= alpha_i <= C,

For L1-regularized L2-loss SVC (-s 5), we solve

min_w \sum |w_j| + C \sum max(0, 1- y_i w^Tx_i)^2

For L1-regularized logistic regression (-s 6), we solve

min_w \sum |w_j| + C \sum log(1 + exp(-y_i w^Tx_i))

For L2-regularized logistic regression (-s 7), we solve

min_alpha  0.5(alpha^T Q alpha) + \sum alpha_i*log(alpha_i) + \sum (C-alpha_i)*log(C-alpha_i) - a constant
s.t.   0 <= alpha_i <= C,

where

Q is a matrix with Q_ij = y_i y_j x_i^T x_j.

For L2-regularized L2-loss SVR (-s 11), we solve

min_w w^Tw/2 + C \sum max(0, |y_i-w^Tx_i|-epsilon)^2

For L2-regularized L2-loss SVR dual (-s 12), we solve

min_beta  0.5(beta^T (Q + lambda I/2/C) beta) - y^T beta + \sum |beta_i|

For L2-regularized L1-loss SVR dual (-s 13), we solve

min_beta  0.5(beta^T Q beta) - y^T beta + \sum |beta_i|
s.t.   -C <= beta_i <= C,

where

Q is a matrix with Q_ij = x_i^T x_j.

If bias >= 0, w becomes [w; w_{n+1}] and x becomes [x; bias].

The primal-dual relationship implies that -s 1 and -s 2 give the same model, -s 0 and -s 7 give the same, and -s 11 and -s 12 give the same.

We implement 1-vs-the rest multi-class strategy for classification. In training i vs. non_i, their C parameters are (weight from -wi)*C and C, respectively. If there are only two classes, we train only one model. Thus weight1*C vs. weight2*C is used. See examples below.

We also implement multi-class SVM by Crammer and Singer (-s 4):

min_{w_m, \xi_i}  0.5 \sum_m ||w_m||^2 + C \sum_i \xi_i
s.t.  w^T_{y_i} x_i - w^T_m x_i >= \e^m_i - \xi_i \forall m,i

where e^m_i = 0 if y_i  = m,
e^m_i = 1 if y_i != m,

Here we solve the dual problem:

min_{\alpha}  0.5 \sum_m ||w_m(\alpha)||^2 + \sum_i \sum_m e^m_i alpha^m_i
s.t.  \alpha^m_i <= C^m_i \forall m,i , \sum_m \alpha^m_i=0 \forall i

where w_m(\alpha) = \sum_i \alpha^m_i x_i,
and C^m_i = C if m  = y_i,
C^m_i = 0 if m != y_i.

# predict Usage

Usage: predict [options] test_file model_file output_file
options:
-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only
-q : quiet mode (no outputs)

Note that -b is only needed in the prediction phase. This is different from the setting of LIBSVM.

# Examples

> train data_file

Train linear SVM with L2-loss function.

> train -s 0 data_file

Train a logistic regression model.

> train -v 5 -e 0.001 data_file

Do five-fold cross-validation using L2-loss svm. Use a smaller stopping tolerance 0.001 than the default 0.1 if you want more accurate solutions.

> train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file

Train four classifiers: positive negative Cp Cn class 1 class 2,3,4. 20 10 class 2 class 1,3,4. 50 10 class 3 class 1,2,4. 20 10 class 4 class 1,2,3. 10 10

> train -c 10 -w3 1 -w2 5 two_class_data_file

If there are only two classes, we train ONE model. The C values for the two classes are 10 and 50.

> predict -b 1 test_file data_file.model output_file

Output probability estimates (for logistic regression only).

# Library Usage

• Function: model* train(const struct problem *prob, const struct parameter *param);

This function constructs and returns a linear classification or regression model according to the given training data and parameters.

struct problem describes the problem:

struct problem
{
int l, n;
int *y;
struct feature_node **x;
double bias;
};

where l is the number of training data. If bias >= 0, we assume that one additional feature is added to the end of each data instance. n is the number of feature (including the bias feature if bias >= 0). y is an array containing the target values. (integers in classification, real numbers in regression) And x is an array of pointers, each of which points to a sparse representation (array of feature_node) of one training vector.

For example, if we have the following training data:

LABEL       ATTR1   ATTR2   ATTR3   ATTR4   ATTR5
-----       -----   -----   -----   -----   -----
1           0       0.1     0.2     0       0
2           0       0.1     0.3    -1.2     0
1           0.4     0       0       0       0
2           0       0.1     0       1.4     0.5
3          -0.1    -0.2     0.1     1.1     0.1

and bias = 1, then the components of problem are:

l = 5
n = 6

y -> 1 2 1 2 3

x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?)
[ ] -> (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?)
[ ] -> (1,0.4) (6,1) (-1,?)
[ ] -> (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?)
[ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?)

struct parameter describes the parameters of a linear classification or regression model:

struct parameter
{
int solver_type;

/* these are for training only */
double eps;             /* stopping criteria */
double C;
int nr_weight;
int *weight_label;
double* weight;
double p;
};

solver_type can be one of L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL. for classification

• L2R_LR L2-regularized logistic regression (primal)
• L2R_L2LOSS_SVC_DUAL L2-regularized L2-loss support vector classification (dual)
• L2R_L2LOSS_SVC L2-regularized L2-loss support vector classification (primal)
• L2R_L1LOSS_SVC_DUAL L2-regularized L1-loss support vector classification (dual)
• MCSVM_CS support vector classification by Crammer and Singer
• L1R_L2LOSS_SVC L1-regularized L2-loss support vector classification
• L1R_LR L1-regularized logistic regression
• L2R_LR_DUAL L2-regularized logistic regression (dual) for regression
• L2R_L2LOSS_SVR L2-regularized L2-loss support vector regression (primal)
• L2R_L2LOSS_SVR_DUAL L2-regularized L2-loss support vector regression (dual)
• L2R_L1LOSS_SVR_DUAL L2-regularized L1-loss support vector regression (dual)

C is the cost of constraints violation. p is the sensitiveness of loss of support vector regression. eps is the stopping criterion.

nr_weight, weight_label, and weight are used to change the penalty for some classes (If the weight for a class is not changed, it is set to 1). This is useful for training classifier using unbalanced input data or with asymmetric misclassification cost.

nr_weight is the number of elements in the array weight_label and weight. Each weight[i] corresponds to weight_label[i], meaning that the penalty of class weight_label[i] is scaled by a factor of weight[i].

If you do not want to change penalty for any of the classes, just set nr_weight to 0.

NOTE To avoid wrong parameters, check_parameter() should be called before train().

struct model stores the model obtained from the training procedure:

struct model
{
struct parameter param;
int nr_class;           /* number of classes */
int nr_feature;
double *w;
int *label;             /* label of each class */
double bias;
};

param describes the parameters used to obtain the model.

nr_class and nr_feature are the number of classes and features, respectively. nr_class = 2 for regression.

The nr_feature*nr_class array w gives feature weights. We use one against the rest for multi-class classification, so each feature index corresponds to nr_class weight values. Weights are organized in the following way

   +------------------+------------------+------------+
| nr_class weights | nr_class weights |  ...
| for 1st feature  | for 2nd feature  |
+------------------+------------------+------------+

If bias >= 0, x becomes [x; bias]. The number of features is increased by one, so w is a (nr_feature+1)*nr_class array. The value of bias is stored in the variable bias.

The array label stores class labels.

• Function: void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target);

This function conducts cross validation. Data are separated to nr_fold folds. Under given parameters, sequentially each fold is validated using the model from training the remaining. Predicted labels in the validation process are stored in the array called target.

The format of prob is same as that for train().

• Function: double predict(const model *model_, const feature_node *x);

For a classification model, the predicted class for x is returned. For a regression model, the function value of x calculated using the model is returned.

• Function: double predict_values(const struct model *model_, const struct feature_node *x, double* dec_values);

This function gives nr_w decision values in the array dec_values. nr_w=1 if regression is applied or the number of classes is two. An exception is multi-class svm by Crammer and Singer (-s 4), where nr_w = 2 if there are two classes. For all other situations, nr_w is the number of classes.

We implement one-vs-the rest multi-class strategy (-s 0,1,2,3,5,6,7) and multi-class svm by Crammer and Singer (-s 4) for multi-class SVM. The class with the highest decision value is returned.

• Function: double predict_probability(const struct model *model_, const struct feature_node *x, double* prob_estimates);

This function gives nr_class probability estimates in the array prob_estimates. nr_class can be obtained from the function get_nr_class. The class with the highest probability is returned. Currently, we support only the probability outputs of logistic regression.

• Function: int get_nr_feature(const model *model_);

The function gives the number of attributes of the model.

• Function: int get_nr_class(const model *model_);

The function gives the number of classes of the model. For a regression model, 2 is returned.

• Function: void get_labels(const model *model_, int* label);

This function outputs the name of labels into an array called label. For a regression model, label is unchanged.

• Function: double get_decfun_coef(const struct model *model_, int feat_idx, int label_idx);

This function gives the coefficient for the feature with feature index = feat_idx and the class with label index = label_idx. Note that feat_idx starts from 1, while label_idx starts from 0. If feat_idx is not in the valid range (1 to nr_feature), then a zero value will be returned. For classification models, if label_idx is not in the valid range (0 to nr_class-1), then a zero value will be returned; for regression models, label_idx is ignored.

• Function: double get_decfun_bias(const struct model *model_, int label_idx);

This function gives the bias term corresponding to the class with the label_idx. For classification models, if label_idx is not in a valid range (0 to nr_class-1), then a zero value will be returned; for regression models, label_idx is ignored.

• Function: const char *check_parameter(const struct problem *prob, const struct parameter *param);

This function checks whether the parameters are within the feasible range of the problem. This function should be called before calling train() and cross_validation(). It returns NULL if the parameters are feasible, otherwise an error message is returned.

• Function: int check_probability_model(const struct model *model);

This function returns 1 if the model supports probability output; otherwise, it returns 0.

• Function: int check_regression_model(const struct model *model);

This function returns 1 if the model is a regression model; otherwise it returns 0.

• Function: int save_model(const char *model_file_name, const struct model *model_);

This function saves a model to a file; returns 0 on success, or -1 if an error occurs.

• Function: struct model *load_model(const char *model_file_name);

This function returns a pointer to the model read from the file, or a null pointer if the model could not be loaded.

• Function: void free_model_content(struct model *model_ptr);

This function frees the memory used by the entries in a model structure.

• Function: void free_and_destroy_model(struct model **model_ptr_ptr);

This function frees the memory used by a model and destroys the model structure.

• Function: void destroy_param(struct parameter *param);

This function frees the memory used by a parameter set.

• Function: void set_print_string_function(void (*print_func)(const char *));

Users can specify their output format by a function. Use set_print_string_function(NULL); for default printing to stdout.

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin.


text:
# Finding equation of circle knowing 1 point, radius and that it touches the x-axis

I'm currently studying circle co-ordinate geometry, and this problem has puzzled me.

Find the equations of the circles of radius $5$, which touch the x-axis, and pass through the point $(3,1)$.

I would imagine if you drew this out, there would be two circles on either side. Also, because we know that they touch the x-axis, then another point A would be $(a - 5, 0)$, where $a$ is the centre of the circle.

The equations I've been using is: $r^2 = (x - a)^2 + (y - b)^2$, as well as the distance formula based on Pythagoras.

Where do I go from here?

Draw a picture. The circle cannot go below the $x$-axis, for if it did and touched the $x$-axis, it could not go through point $(3,1)$.
So the centre $(a,b)$ is above the $x$-axis. The distance from $(a,b)$ to the $x$-axis is the radius $5$ of the circle, so $b=5$. It follows that the circle has equation of the shape $$(x-a)^2+(y-5)^2=25.$$ Plug in $x=3$, $y=1$. We get an equation in $a$. Solve for $a$. There will be two solutions, both "nice."


text:
# Range concatenation grammars which are unambiguous as (and are NOT CFL)?

Are there any range concatenation grammars that

1. generate only one possible parse tree
2. are not also context-free?
• Write a grammar for $\{a^nb^nc^n\mid n>0\}$. Chances are it will be unambiguous. – Sylvain Aug 15 '17 at 18:50


text:
# Prove that one of any three consecutive positive integers must be divisible by 3.

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To do:

We have to prove that one of any three consecutive positive integers must be divisible by 3.

Solution:

Let $a = n, b = n + 1$ and $c =n + 2$

Order triplet is $(a, b, c) = (n, n + 1, n + 2)$

where, $n$ is any positive integer

At $n = 1$

$(a, b, c) = (1, 1 + 1, 1 + 2)$

$= (1, 2, 3)$

At $n = 2$

$(a, b, c) = (2, 2 + 1, 2 + 2)$

$= (2, 3, 4)$

At $n = 3$

$(a, b,c) = (3, 3 + 1, 3 + 2)$

$= (3,4,5)$

At $n =4$

$(a,b, c) =(4, 4 + 1, 4 +2)$

$= (4, 5, 6)$

At $n = 5$

$(a, b,c) = (5, 5 + 1, 5 +2)$

$= (5,6,7)$

At $n = 6$

$(a,b, c) = (6, 6 + 1, 6 + 2)$

$= (6,7,8)$

At $n = 7$

$(a, b,c) = (7, 7 +1, 7 + 2)$

$= (7,8,9)$

At $n = 8$

$(a, b,c) = (8,8+ 1, 8+ 2)$

$= (8,9,10)$

We observe that each triplet consists of one and only one number which is a multiple of 3.

Hence, one of any three consecutive positive integers must be divisible by 3.

Updated on 10-Oct-2022 13:27:08


text:
# Methodology

## Dynamic model: perturbing forces

### Gravity model

Gravity is a complex force to model. The Earth is not a perfect sphere so the distribution of its mass is non-spherical. This implies that the gravitational force varies as a function of the position. A good approach that takes into account the non-spherical portion of the mass distribution of the Earth is to represent the gravitational potential $$\left ( U \right )$$ as a decomposition of spherical harmonics (Vallado and McClain, 2007):

where $$r$$ is the distance of the satellite from the center of the Earth, $$\lambda_{sat}$$ and $$\phi_{gc_{sat}}$$ are the longitude and geocentric latitude of the satellite, $$P_{l,m}$$ are the Legendre functions, $$C_{l,m}$$ and $$S_{l,m}$$ are the gravitational coefficients, $$l$$ and $$m$$ are the degree and order of the decomposition, and $$\mu$$ and $$R_{\bigoplus}$$ are the gravitational parameter $$\left( \mu \right.$$ = 398,600.442 km$$^3$$/s$$\left. ^2 \right)$$ and mean equatorial radius of the Earth $$\left(\right.$$= 6,378.137 km$$\left. \right)$$, as defined in the World Geodetic System 1984 (WGS 84) (National Imagery and Mappin Agency, 2000). The coefficients $$C_{l,m}$$ and $$S_{l,m}$$ used in SpOCK are taken from the Earth Gravitational Model 1996 (EGM96) (Lemoine et. al, 1997). The gravitational potential of a simple sphere is the first term of the right hand side $$\left ( \frac{\mu}{r} \right )$$. The double summation describes the perturbation due to the non-spherical distribution of the Earth mass. This spherical harmonic decomposition breaks into three categories (Vallado and McClain, 2007):

• zonal harmonics, that represent bands of latitude $$\left (m = 0 \right)$$;
• sectorial harmonics, that represent bands of longitude $$\left (l = m\right)$$;
• tesseral harmonics, that represent tiles $$\left (l \ne m \ne 0\right)$$.

The strongest perturbation is due to the first zonal harmonic $$C_{2,0}$$, more commonly noted $$J_2$$ $$\left( J_2 = \right.$$ 0.0010826267$$\left. \right )$$, which corresponds to the equatorial bulge due to the Earth's rotation causing it to be oblate.

### Atmospheric drag

For Low Earth Orbits (LEO) satellites below $$\sim$$ 500 km, atmospheric drag is the main force acting on a satellite, after gravity, and can not be neglected for accurate orbit calculations. The drag acceleration $$\boldsymbol{a_{\text{drag}}}$$ of a simple surface is represented by (Vallado and McClain, 2007):

where $$C_D$$, $$A$$, and $$m$$ are the drag coefficient, area projected towards the velocity vector (discussed below) and mass of the surface respectively, and $$\boldsymbol{v_{rel}}$$ is the satellite velocity with respect to the moving atmosphere of density $$\rho$$.

Atmospheric drag is hard to model, mainly because the thermospheric density is very complex to model. It is driven by many phenomema that are themselves hard to predict, such as the solar X-Ray and EUV fluxes, aurora, high latitude Joule heating, which are often proxied by indices such as F10.7, Ap, Kp, and Hemispheric Power. Even if the drivers are well known, deriving the density is still a challenge for the scientific community. Many thermosphere models attempt to model it. The High Accuracy Satellite Drag Model (HASDM, Storz et. al, 2005) was developed in 2002 by the Air Force Space Battlelab to improve satellite trajectory prediction accuracy, by analyzing the effect of drag on trajectories of LEO satellites. It has been combined to the empirical density model Jacchia-Bowman 2008 (Bowman et. al, 2008), an improved version of the Jacchia-Bowman 2006 model that is based on Jacchia's diffusion equations, in the Jacchia-Bowman-HASDM 2009 (Newman et. al, 2015), and is currently used at the Joint Space Operations Center (JSpOC). Other examples of thermosphere models include the NRLMSISE-00 Model 2001 (NRLMSIS-00e, Picone et. al, 2002), the Global Ionosphere Thermosphere Model (GITM, Ridley et. al, 2006), and the Thermosphere-Ionosphere Electrodynamics Global Circulation Model (TIEGCM, Roble et. al, 1994).

### Third body perturbation

Gravitational perturbations by a third body (Sun and Moon) are also modeled in SpOCK. The accelerations due to these perturbations can be written as (Vallado and McClain, 2007):

where $$\mu_3$$ is the gravitational parameter of the third body (Sun or Moon), $$\boldsymbol{r_{\text{sat, 3}}}$$ the vector from the satellite to the third body, and $$\boldsymbol{r_{\text{Earth, 3}}}$$ the vector from the Earth to the third body. The first term represents the gravitational force of the third body on the satellite. The second term, called the indirect effect, corresponds to the gravitational force of the third body on the Earth (Vallado and McClain, 2007). The National Aeronautics and Space Administration (NASA) SPICE Toolkit is used to calculate the position of the Sun and the Moon at every time step of the propagation.

where $$A$$ is the cross sectional area as seen by the Sun (discussed below), $$m$$ the mass of the satellite, $$C_r$$ the solar radiation coefficient, $$L_\text{Sun}$$ the luminosity of the Sun $$\left (L_\text{Sun} = 3.823 \right.$$ $$\times$$ 10$$^{26}$$ $$\left.W\right )$$, $$c$$ the speed of light $$\left(c = \right.$$ 299,792.458 km/s$$\left.\right)$$, and $$\boldsymbol{r_{\text{sat, Sun}}}$$ the vector from the satellite to the Sun.


text:
I am trying to figure out on my Arduino UNO how to read analog input using port manipulation. I have found a lot of tutorials on how to do that for digitalRead / digitalWrite, but nothing with analogRead / Write.

I want to read the analog data coming from the FSR sensor but I cannot figure out how to do that.

I found that for analog pins I have to use the C port of ATMega, and I tried by testing the code below. but the only thing I get as result is either 0 or 1 when I get over half of the read values.

How can I get the values as an integer by using port manipulation? Or a double/float, the format of the result is not that important as long it is not boolean.

• Why not use analogRead()? May 31, 2021 at 8:04
• I have to program an ATMega using AVR codevision and as I noticed I have to use port and register manipulation, it's not like arduino ide or smth else and i am trying to get how to do that May 31, 2021 at 15:41

The analog readings for pins are obtained using the ADC peripheral, not a GPIO port. Additionally, you might not get the performance benefits you are looking for, since the relative overhead of analogRead is not as large as the overhead of digitalRead, and most of the time is spent actually waiting for the ADC to do a conversion.

The exact registers and details are given in the atmega328p datasheet, but I'll give an overview of a simple approach. Of course, you'll want to consult the datasheet for full details and explanations.

First, you'll need to enable the ADC and set the desired prescaler for an ADC clock of between 50 and 200 kHz by setting the ADEN, ADPS2, ADPS1, and ADPS0 bits in ADCSRA. For an Arduino clocked at 16 MHz, this gives an ADC clock of 125 kHz, well within the required range:

Datasheet page 258

You'll then need to set the high bits of ADMUX, which specify the analog reference you want to use (external, 5 V AVCC, or 1.1 V internal reference). The default behavior of Arduino's analogRead is the 5 V AVCC reference, meaning that you'd need to set the REFS0 and clear the REFS1 bit.

Datasheet page 257

You can then select the input you want to read by setting the lower four bits of ADMUX (following table 24-4 on page 258), and then set the ADSC (A/D Start Conversion) of ADCSRA to start a conversion. You can then loop waiting for ADSC to return to 0 indicating the conversion finished (or set an interrupt instead); once the conversion is done the results will be present in ADCH and ADCL.

However, if you're looking to optimize the time for reading a single analog port, you'll want to use a free-running mode. By setting ADATE in ADCSRA to 1 and leaving ADCSRB at all-zeros (default indicating free-running), the ADC will automatically perform analog reads, putting the result into ADCH and ADCL when it's ready -- you can read it at any time, for only the cost of two port reads. Note that in this case, you'll want to read ADCH and ADCL atomically by reading ADC (a syntactic sugar that leads to the compiler reading the two registers atomically):

 uint16_t result = ADC;

If you wish to save power, you may want to set the appropriate bits of DIDR0 as well; digital input circuitry can sometimes use excess power if an analog input not close to 0 or 5 V is provided, and disabling the digital input using DIDR0 will prevent this excess power consumption.

• So basically, I guess I have to do something like this guy does starting with min 11 youtube.com/watch?v=IhtD_VSoURk Sorry if I don't get everything you say, I'm beginner with arduino embedded programming May 30, 2021 at 23:09
• @Shortcircuit Potentially; I'm having a bit of trouble following what's going on in the video, but that looks right. Getting a better understanding of the concepts (i.e. what the registers mean and how to interact with them) should help clarify things. May 30, 2021 at 23:17
• yeah, same, it's pretty hard to follow it but I find it like a visual way to follow while reading your instructions and might help I guess, thank you! May 31, 2021 at 15:39

The complete details are described in the atmega328 datasheet starting on page 205.

As @nanofarad mentions, particular attention needs to be paid attention to setting up the ADC clock which should be between 50 kHz and 200 kHz. The ADC clock is derived from the system clock using one of eight pre-scalar settings set in the ADCSRA register.

Except for setting the ADC clock frequency, this gist shows how to perform an analog read:

https://gist.github.com/Wollw/2396604

There are bits in the ADMUX register which need to be set up, a conversion is started by setting the ADSC bit in the ADCSRA register and then you have to wait until the ADSC bit is set to zero.

You can also have an interrupt generated when a conversion is completed. See this blog post for more details:

http://www.glennsweeney.com/tutorials/interrupt-driven-analog-conversion-with-an-atmega328p

There are additional features available when controlling the ADC directly such as selecting the source for AREF, having a conversion automatically start on an event, left adjusting the conversion result.

• @ErikR so basically the mistake i am doing is using PORT/PIN/DDR C instead of ADC and ADMUX and their configuration and setup. Thought if B and D ports can be used just like that the analog pins are alike May 30, 2021 at 22:47
• Yes, the 10-bit result of an ADC conversion is found in the ADCL and ADCH registers (available as the 16-bit ADC register in C). What's your question about the B and D ports? May 30, 2021 at 22:53


text:
# Difference between revisions of "Hydrodynamic metamaterials: Microfabricated arrays to steer, refract and focus streams of biomaterials"

Original entry: Warren Lloyd Ung, APPHY 225, Fall 2009

"Hydrodynamic metamaterials: Microfabricated arrays to steer, refractt, and focus streams of biomaterials"
Keith J. Morton, Kevin Loutherback, David W. Inglis, Ophelia K. Tsui, James C. Sturm, Stephen Y. Chou, and Robert H. Austin.
Proceedings of the National Academy of Science.

## Soft Matter Keywords

Hydrodynamic metamaterials, microfluidics, biomaterials, separation

Figure 1: Hydrodynamic metamaterial: an asymmetric array of posts (A) schematic and (B) fluoresecne image of a particle larger than the critical size (red) and smaller than the critical size (green).
Figure 2: Focusing of flowing particles: (A) The analogous case for light is an axicon lens, which focuses light to a line, (B) Schematic of the different regions of distinct metamaterials, (C) Micrograph of the distinct metamaterial regions, (D) fluorescence image showing particle focusing by this geometry of posts.
Figure 3: Steering of cells through a microfluidic channel

## Summary

Hydrodynamic metamaterials are microfabricated structures that have can be used to manipulate particles to flow along particular paths. In many ways, they are analogous to traditional optical materials, except rather than modifying the propagation of electromagnetic light waves, these metamaterials modify the propagation of particles through a microfluidic channel.

The hydrodynamic metamaterial discussed in here is an asymmetric array of posts. The asymmetry arises, because each subsequent column of posts is vertically offset from the previous column by a small distance. As a result, the rows of posts are at an angle, $\alpha$, relative to the direction of flow (Figure 1). Small particles and molecules follow the streamlines of fluid through the channel, and as such, they simply follow the direction of bulk fluid flow through the channel, while moving around any posts in their way. On the other hand, particles larger than some critical size cannot do the same, they cannot fully move around the posts, so each time they encounter a new post, they are deflected it. The large particles, thus, follow the asymmetry of the channel, and propagate at the angle $\alpha$ relative to the flow direction. This ability to segregate particles according to their sizes can be compared with the ability of birefringent crystals to separate different polarizations of light in space.

These metamaterials offer exquisite control over the flow direction of particles in solution. By putting metamaterials with different parameters next to one another, it is possible to create devices to control the direction, along which particles of different sizes move. It is also possible to place several different arrays within a single channel to achieve complex devices. The authors showcase a range of possible applications for these simple microstructures, each time demonstrating its analogy in optics.

## Soft Matter Discussion

The parameters of the array uniquely define the angle, at which large particles are deflected, and also the critical size of particles, at which particles transition from passing around the posts in the array to being deflected along the rows. The authors define four parameters, the horizontal spacing $\lambda$, the vertical gap between posts, G, the vertical offset between rows, $\delta$, and the obstacle size, D.

Qualitatively, for a given G, the critical particle size decreases as the angle is increased

$\alpha = tan^{-1}\frac{\delta}{\lambda}$

For a fixed particle size and angle, the critical particle size decreases with G. These behave more or less what as we would expect.

The likely cause of the observed results is that the hydraulic object is much larger than the cross-sectional gap between the posts. Objects with large hydraulic diameters are less likely to fit through the space between the post. We would also expect the critical size to change as a function of flow rate. The inertia of particles in the flow is a parameter that we expect to contribute to which mode the particle finds itself in. This dependence is not addressed in the paper.

## Applications

Using these asymmetric arrays of posts as building blocks, it is possible to create devices which refract, focus or disperse particles selectively based on size. This size can be tuned to select for the distinct particles of interest in a system, whether they be surfactant-stabilized droplets, vesicles, cells or beads. As a result, this technique is applicable to many relevant systems of interest.

Although these ideas are applicable, in general, to nearly any system where micro-scale particles should be manipulated based on size, because this is intrinsically a microfluidic technique, many of the most interesting applications are in the field of microfluidics. Because these posts are already within a microfluidic channel and they are compatible with the standard soft lithography methds, this is a powerful tool, which can already be integrated directly with many of the other complex microfluidic systems. For instance, one can imagine a system for studying mitotic cells, in which we separate mitotic cells based on their size from interphase cells in a label-free manner. This could be combined with methods for optical interrogation and on-chip cell culture techniques to achieve a highly integrated system for studying cells.

Another potential microfluidics application would be in separating a polydisperse suspension of particles into a set of monodisperse flows. These flows could then be split and routed to other parts of the microfluidic device for highly parallel experiments, in which particle size is a parameter of interest.