A Medley of Potpourri

Friday, August 29, 2025

Solar-cell efficiency

https://en.wikipedia.org/wiki/Solar-cell_efficiency

Solar-cell efficiency is the portion of energy in the form of sunlight that can be converted via photovoltaics into electricity by the solar cell.

The efficiency of the solar cells used in a photovoltaic system, in combination with latitude and climate, determines the annual energy output of the system. For example, a solar panel with 20% efficiency and an area of 1 m² produces 200 kWh/yr at Standard Test Conditions if exposed to the Standard Test Condition solar irradiance value of 1000 W/m² for 2.74 hours a day. Usually solar panels are exposed to sunlight for longer than this in a given day, but the solar irradiance is less than 1000 W/m² for most of the day. A solar panel can produce more when the Sun is high in Earth's sky and produces less in cloudy conditions, or when the Sun is low in the sky. The Sun is lower in the sky in the winter.

Two location dependent factors that affect solar PV yield are the dispersion and intensity of solar radiation. These two variables can vary greatly between each country. The global regions that have high radiation levels throughout the year are the Middle East, Northern Chile, Australia, China, and Southwestern USA.In a high-yield solar area like central Colorado, which receives annual insolation of 2000 kWh/m²/year, a panel can be expected to produce 400 kWh of energy per year. However, in Michigan, which receives only 1400 kWh/m²/year, annual energy yield drops to 280 kWh for the same panel. At more northerly European latitudes, yields are significantly lower: 175 kWh annual energy yield in southern England under the same conditions.

Schematic of charge collection by solar cells. Light transmits through transparent conducting electrode creating electron hole pairs, which are collected by both the electrodes. The absorption and collection efficiencies of a solar cell depend on the design of transparent conductors and active layer thickness.

Several factors affect a cell's conversion efficiency, including its reflectance, thermodynamic efficiency, charge carrier separation efficiency, charge carrier collection efficiency and conduction efficiency values. Because these parameters can be difficult to measure directly, other parameters are measured instead, including quantum efficiency, open-circuit voltage (V_OC) ratio, and § Fill factor. Reflectance losses are accounted for by the quantum efficiency value, as they affect external quantum efficiency. Recombination losses are accounted for by the quantum efficiency, V_OC ratio, and fill factor values. Resistive losses are predominantly accounted for by the fill factor value, but also contribute to the quantum efficiency and V_OC ratio values.

As of 2024, the world record for solar cell efficiency is 47.6%, set in May 2022 by Fraunhofer ISE, with a III-V four-junction concentrating photovoltaic (CPV) cell. This beat the previous record of 47.1%, set in 2019 by multi-junction concentrator solar cells developed at National Renewable Energy Laboratory (NREL), Golden, Colorado, USA, which was set in lab conditions, under extremely concentrated light. The record in real-world conditions is held by NREL, who developed triple junction cells with a tested efficiency of 39.5%.

Factors affecting energy conversion efficiency

The factors affecting energy conversion efficiency were expounded in a landmark paper by William Shockley and Hans Queisser in 1961. See Shockley–Queisser limit for more detail.

Thermodynamic-efficiency limit and infinite-stack limit

The Shockley–Queisser limit for the efficiency of a single-junction solar cell under unconcentrated sunlight at 273 K. This calculated curve uses actual solar spectrum data, and therefore the curve is wiggly from IR absorption bands in the atmosphere. This efficiency limit of ~34% can be exceeded by multijunction solar cells.

If one has a source of heat at temperature $T s$ and cooler heat sink at temperature $T c$ , the maximum theoretically possible value for the ratio of work (or electric power) obtained to heat supplied is $1- T c / T s$ , given by a Carnot heat engine. If we take 6000 K for the temperature of the sun and 300 K for ambient conditions on earth, this comes to 95%. In 1981, Alexis de Vos and Herman Pauwels showed that this is achievable with a stack of an infinite number of cells with band gaps ranging from infinity (the first cells encountered by the incoming photons) to zero, with a voltage in each cell very close to the open-circuit voltage, equal to 95% of the band gap of that cell, and with 6000 K blackbody radiation coming from all directions. However, the 95% efficiency thereby achieved means that the electric power is 95% of the net amount of light absorbed – the stack emits radiation as it has non-zero temperature, and this radiation must be subtracted from the incoming radiation when calculating the amount of heat being transferred and the efficiency. They also considered the more relevant problem of maximizing the power output for a stack being illuminated from all directions by 6000 K blackbody radiation. In this case, the voltages must be lowered to less than 95% of the band gap (the percentage is not constant over all the cells). The maximum theoretical efficiency calculated is 86.8% for a stack of an infinite number of cells, using the incoming concentrated sunlight radiation. When the incoming radiation comes only from an area of the sky the size of the sun, the efficiency limit drops to 68.7%.

Ultimate efficiency

Normal photovoltaic systems however have only one p–n junction and are therefore subject to a lower efficiency limit, called the "ultimate efficiency" by Shockley and Queisser. Photons with an energy below the band gap of the absorber material cannot generate an electron-hole pair, so their energy is not converted to useful output, and only generates heat if absorbed. For photons with an energy above the band gap energy, only a fraction of the energy above the band gap can be converted to useful output. When a photon of greater energy is absorbed, the excess energy above the band gap is converted to kinetic energy of the carrier combination. The excess kinetic energy is converted to heat through phonon interactions as the kinetic energy of the carriers slows to equilibrium velocity. Traditional single-junction cells with an optimal band gap for the solar spectrum have a maximum theoretical efficiency of 33.16%, the Shockley–Queisser limit.

Solar cells with multiple band gap absorber materials improve efficiency by dividing the solar spectrum into smaller bins where the thermodynamic efficiency limit is higher for each bin.

Quantum efficiency

When a photon is absorbed by a solar cell it can produce an electron-hole pair. One of the carriers may reach the p–n junction and contribute to the current produced by the solar cell; such a carrier is said to be collected. Or, the carriers recombine with no net contribution to cell current.

Quantum efficiency refers to the percentage of photons that are converted to electric current (i.e., collected carriers) when the cell is operated under short circuit conditions. The two types of quantum that are usually referred to when talking about solar cells are external and internal. External quantum efficiency (EQE) relates to the measurable properties of the solar cell. The "external" quantum efficiency of a silicon solar cell includes the effect of optical losses such as transmission and reflection. Measures can be taken to reduce these losses. The reflection losses, which can account for up to 10% of the total incident energy, can be dramatically decreased using a technique called texturization, a light trapping method that modifies the average light path.

The internal quantum efficiency (IQE) gives insight into the internal material parameters like the absorption coefficient or internal luminescence quantum efficiency. IQE is mainly used to aid the understanding of the potential of a certain material rather than a device.

Quantum efficiency is most usefully expressed as a spectral measurement (that is, as a function of photon wavelength or energy). Since some wavelengths are absorbed more effectively than others, spectral measurements of quantum efficiency can yield valuable information about the quality of the semiconductor bulk and surfaces.

Quantum efficiency is not the same as overall energy conversion efficiency, as it does not convey information about the fraction of power that is converted by the solar cell.

Maximum power point

A solar cell may operate over a wide range of voltages (V) and currents (I). By increasing the resistive load on an irradiated cell continuously from zero (a short circuit) to a very high value (an open circuit) one can determine the maximum power point, the point that maximizes V×I; that is, the load for which the cell can deliver maximum electrical power at that level of irradiation. (The output power is zero in both the short circuit and open circuit extremes).

The maximum power point of a solar cell is affected by its temperature. Knowing the technical data of certain solar cell, its power output at a certain temperature can be obtained by $P (T) = P_{S T C} + \frac{d P}{d T} (T_{c e l l} - T_{S T C})$ , where $P_{S T C}$ is the power generated at the standard testing condition; $T_{c e l l}$ is the actual temperature of the solar cell.

A high quality, monocrystalline silicon solar cell, at 25 °C cell temperature, may produce 0.60 V open-circuit (V_OC). The cell temperature in full sunlight, even with 25 °C air temperature, is probably close to 45 °C, reducing the open-circuit voltage to 0.55 V per cell. The voltage drops modestly, with this type of cell, until the short-circuit current is approached (I_SC). Maximum power (with 45 °C cell temperature) is typically produced with 75% to 80% of the open-circuit voltage (0.43 V in this case) and 90% of the short-circuit current. This output can be up to 70% of the V_OC x I_SC product. The short-circuit current (I_SC) from a cell is nearly proportional to the illumination, while the open-circuit voltage (V_OC) may drop only 10% with an 80% drop in illumination. Lower-quality cells have a more rapid drop in voltage with increasing current and could produce only 1/2 V_OC at 1/2 I_SC. The usable power output could thus drop from 70% of the V_OC x I_SC product to 50% or even as little as 25%. Vendors who rate their solar cell "power" only as V_OC x I_SC, without giving load curves, can be seriously distorting their actual performance.

The maximum power point of a photovoltaic varies with incident illumination. For example, accumulation of dust on photovoltaic panels reduces the maximum power point. Recently, new research to remove dust from solar panels has been developed by utilizing electrostatic cleaning systems. In such systems, an applied electrostatic field at the surface of the solar panels causes the dust particles to move in a "flip-flop" manner. Then, due to gravity and the fact that the solar panels are slightly slanted, the dust particles get pulled downward by gravity. These systems only require a small power consumption and enhance the performance of the solar cells, especially when installed in the desert, where dust accumulation contributes to decreasing the solar panel's performance. Also, for systems large enough to justify the extra expense, a maximum power point tracker tracks the instantaneous power by continually measuring the voltage and current (and hence, power transfer), and uses this information to dynamically adjust the load so the maximum power is always transferred, regardless of the variation in lighting.

Fill factor

Another defining term in the overall behaviour of a solar cell is the fill factor (FF). This factor is a measure of quality of a solar cell. This is the available power at the maximum power point (P_m) divided by the open circuit voltage (V_OC) and the short circuit current (I_SC):

F F = \frac{P_{m}}{V_{O C} \times I_{S C}} = \frac{η \times A_{c} \times G}{V_{O C} \times I_{S C}} .

The fill factor can be represented graphically by the IV sweep, where it is the ratio of the different rectangular areas.

The fill factor is directly affected by the values of the cell's series, shunt resistances and diodes losses. Increasing the shunt resistance (R_sh) and decreasing the series resistance (R_s) lead to a higher fill factor, thus resulting in greater efficiency, and bringing the cell's output power closer to its theoretical maximum.

Typical fill factors range from 50% to 82%. The fill factor for a normal silicon PV cell is 80%.

Comparison

Energy conversion efficiency is measured by dividing the electrical output by the incident light power. Factors influencing output include spectral distribution, spatial distribution of power, temperature, and resistive load. IEC standard 61215 is used to compare the performance of cells and is designed around standard (terrestrial, temperate) temperature and conditions (STC): irradiance of 1 kW/m², a spectral distribution close to solar radiation through AM (airmass) of 1.5 and a cell temperature 25 °C. The resistive load is varied until the peak or maximum power point (MPP) is achieved. The power at this point is recorded as Watt-peak (Wp). The same standard is used for measuring the power and efficiency of PV modules.

Air mass affects output. In space, where there is no atmosphere, the spectrum of the Sun is relatively unfiltered. However, on Earth, air filters the incoming light, changing the solar spectrum. The filtering effect ranges from Air Mass 0 (AM0) in space, to approximately Air Mass 1.5 on Earth. Multiplying the spectral differences by the quantum efficiency of the solar cell in question yields the efficiency. Terrestrial efficiencies typically are greater than space efficiencies. For example, a silicon solar cell in space might have an efficiency of 14% at AM0, but 16% on Earth at AM 1.5. Note, however, that the number of incident photons in space is considerably larger, so the solar cell might produce considerably more power in space, despite the lower efficiency as indicated by reduced percentage of the total incident energy captured.

Solar cell efficiencies vary from 6% for amorphous silicon-based solar cells to 44.0% with multiple-junction production cells and 44.4% with multiple dies assembled into a hybrid package. Solar cell energy conversion efficiencies for commercially available multicrystalline Si solar cells are around 14–19%. The highest efficiency cells have not always been the most economical – for example a 30% efficient multijunction cell based on exotic materials such as gallium arsenide or indium selenide produced at low volume might well cost one hundred times as much as an 8% efficient amorphous silicon cell in mass production, while delivering only about four times the output.

However, there is a way to "boost" solar power. By increasing the light intensity, typically photogenerated carriers are increased, increasing efficiency by up to 15%. These so-called "concentrator systems" have only begun to become cost-competitive as a result of the development of high efficiency GaAs cells. The increase in intensity is typically accomplished by using concentrating optics. A typical concentrator system may use a light intensity 6–400 times the Sun, and increase the efficiency of a one sun GaAs cell from 31% at AM 1.5 to 35%.

A common method used to express economic costs is to calculate a price per delivered kilowatt-hour (kWh). The solar cell efficiency in combination with the available irradiation has a major influence on the costs, but generally speaking the overall system efficiency is important. Commercially available solar cells (as of 2006) reached system efficiencies between 5 and 19%.

Undoped crystalline silicon devices are approaching the theoretical limiting efficiency of 29.43%. In 2017, efficiency of 26.63% was achieved in an amorphous silicon/crystalline silicon heterojunction cell that place both positive and negative contacts on the back of the cell.

Energy payback

The energy payback time is the recovery time required for generating the energy spent for manufacturing a modern photovoltaic module. An 2008 estimate puts it at from 1 to 4 yearsdepending on the module type and location. With a typical lifetime of 20 to 30 years, this means that modern solar cells would be net energy producers, i.e., they would generate more energy over their lifetime than the energy expended in producing them. Generally, thin-film technologies—despite having comparatively low conversion efficiencies—achieve significantly shorter energy payback times than conventional systems (often < 1 year).

A study published in 2013 found that energy payback time was between 0.75 and 3.5 years with thin film cells being at the lower end and multicrystalline silicon (multi-Si) cells having a payback time of 1.5–2.6 years. A 2015 review assessed the energy payback time and EROI of solar photovoltaics. In this meta study, which uses an insolation of 1,700 kWh/m²/year and a system lifetime of 30 years, mean harmonized EROIs between 8.7 and 34.2 were found. Mean harmonized energy payback time varied from 1.0 to 4.1 years. Crystalline silicon devices achieve on average an energy payback period of 2 years.

Like any other technology, solar cell manufacture is dependent on the existence of a complex global industrial manufacturing system. This includes the fabrication systems typically accounted for in estimates of manufacturing energy; the contingent mining, refining and global transportation systems; and other energy intensive support systems including finance, information, and security systems. The difficulty in measuring such energy overhead confers some uncertainty on any estimate of payback times.

Technical methods of improving efficiency

Choosing optimum transparent conductor

The illuminated side of some types of solar cells, thin films, have a transparent conducting film to allow light to enter into the active material and to collect the generated charge carriers. Typically, films with high transmittance and high electrical conductance such as indium tin oxide, conducting polymers or conducting nanowire networks are used for the purpose. There is a trade-off between high transmittance and electrical conductance, thus optimum density of conducting nanowires or conducting network structure should be chosen for high efficiency.

Promoting light scattering

The inclusion of light-scattering effects in solar cells is a photonic strategy to increase the absorption for the lower-energy sunlight photons (chiefly in near-infrared range) for which the photovoltaic material presents reduced absorption coefficient. Such light-trapping scheme is accomplished by the deviation of the light rays from the incident direction, thereby increasing their path length in the cells' absorber. Conventional approaches used to implement light diffusion are based on textured rear/front surfaces, but many alternative optical designs have been demonstrated with promising results based in diffraction gratings, arrays of metal or dielectric nano/micro particles, wave-optical micro-structuring, among others. When applied in the devices' front these structures can act as geometric anti-reflective coatings, simultaneously reducing the reflection of out-going light.

For instance, lining the light-receiving surface of the cell with nano-sized metallic studs can substantially increase the cell efficiency. Light reflects off these studs at an oblique angle to the cell, increasing the length of the light path through the cell. This increases the number of photons absorbed by the cell and the amount of current generated. The main materials used for the nano-studs are silver, gold, and aluminium. Gold and silver are not very efficient, as they absorb much of the light in the visible spectrum, which contains most of the energy present in sunlight, reducing the amount of light reaching the cell. Aluminium absorbs only ultraviolet radiation, and reflects both visible and infra-red light, so energy loss is minimized. Aluminium can increase cell efficiency up to 22% (in lab conditions).

Anti-reflective coatings and textures

Anti-reflective coatings are engineered to reduce the sunlight reflected from the solar cells, therefore enhancing the light transmitted into the photovoltaic absorber. This can be accomplished by causing the destructive interference of the reflected light waves, such as with coatings based on the front (multi-)layer composition, and/or by geometric refractive-index matching caused by the surface topography, with many architectures inspired by nature. For example, the nipple-array, a hexagonal array of subwavelength conical nanostructures, can be seen at the surface of the moth's eyes. It was reported that utilizing this sort of surface architecture minimizes the reflection losses by 25%, converting the additional captured photon to a 12% increase in a solar cell's energy.

The use of front micro-structures, such as those achieved with texturizing or other photonic features, can also be used as a method to achieve anti-reflectiveness, in which the surface of a solar cell is altered so that the impinging light experiences a gradually increasing effective refractive-index when travelling from air towards the photovoltaic material. These surfaces can be created by etching or using lithography. Concomitantly, they promote light scattering effects that further enhance the absorption, particularly of the longer wavelength sunlight photons. Adding a flat back surface in addition to texturizing the front surface further helps to trap the light within the cell, thus providing a longer optical path.

Radiative cooling

An increase in solar cell temperature of approximately 1 °C causes an efficiency decrease of about 0.45%. To prevent this, a transparent silica crystal layer can be applied to solar panels. The silica layer acts as a thermal black body, which emits heat as infrared radiation into space, cooling the cell up to 13 °C. Radiative cooling can thus extend the life of solar cells. Full-system integration of solar energy and radiative cooling is referred to as a combined SE–RC system, which have demonstrated higher energy gain per unit area when compared to non-integrated systems.

Rear surface passivation

Surface passivation is critical to solar cell efficiency. Many improvements have been made to the front side of mass-produced solar cells, but the aluminium back-surface is impeding efficiency improvements. The efficiency of many solar cells has benefitted by creating so-called passivated emitter and rear cells (PERCs). The chemical deposition of a rear-surface dielectric passivation layer stack that is also made of a thin silica or aluminium oxide film topped with a silicon nitride film helps to improve efficiency in silicon solar cells. This helped increase cell efficiency for commercial Cz-Si wafer material from just over 17% to over 21% by the mid-2010s, and the cell efficiency for quasi-mono-Si to a record 19.9%.

Concepts of the rear surface passivation for silicon solar cells has also been implemented for CIGS solar cells. The rear surface passivation shows the potential to improve the efficiency. Al₂O₃ and SiO₂ have been used as the passivation materials. Nano-sized point contacts on Al₂O₃ layer and line contacts on SiO2 layer provide the electrical connection of CIGS absorber to the rear electrode Molybdenum. The point contacts on the Al₂O₃ layer are created by e-beam lithography and the line contacts on the SiO₂ layer are created using photolithography. Also, the implementation of the passivation layers does not change the morphology of the CIGS layers.

Thin film materials

Although not constituting a direct strategy to improve efficiency, thin film materials show a lot of promise for solar cells in terms of low costs and adaptability to existing structures and frameworks in technology. Since the materials are so thin, they lack the optical absorption of bulk material solar cells. Attempts to correct this have been demonstrated, such as light-trapping schemes promoting light scattering. Also important is thin film surface recombination. Since this is the dominant recombination process of nanoscale thin-film solar cells, it is crucial to their efficiency. Adding a passivating thin layer of silicon dioxide could reduce recombination.

Tandem cells

Tandem solar cells combine two materials to increase efficiency. In 2022 a device was announced that combined multiple perovskite with multiple layers of silicon. Perovskites demonstrate a remarkable ability to efficiently capture and convert blue light, complementing silicon, which is particularly adept at absorbing red and infrared wavelengths. This unique synergy between perovskites and silicon in solar cell technologies allows for a more comprehensive absorption of the solar spectrum, enhancing the overall efficiency and performance of photovoltaic devices. The cell achieved 32.5% efficiency.

Biochar

From Wikipedia, the free encyclopedia

Biochar is a form of charcoal, sometimes modified, that is intended for organic use, as in soil. It is the lightweight black remnants remaining after the pyrolysis of biomass, consisting of carbon and ashes. Despite its name, biochar is sterile immediately after production and only gains biological life following assisted or incidental exposure to biota. Biochar is defined by the International Biochar Initiative as the "solid material obtained from the thermochemical conversion of biomass in an oxygen-limited environment".

Biochar is mainly used in soils to increase soil aeration, reduce soil emissions of greenhouse gases, reduce nutrient leaching, reduce soil acidity, and potentially increase the water content of coarse soils. Biochar application may increase soil fertility and agricultural productivity. However, when applied excessively or made from feedstock unsuitable for the soil type, biochar soil amendments also have the potential for negative effects, including harming soil biota, reducing available water content, altering soil pH, and increasing salinity.

Beyond soil application, biochar can be used for slash-and-char farming, for water retention in soil, and as an additive for animal fodder. There is an increasing focus on the potential role of biochar application in global climate change mitigation. Due to its refractory stability, biochar can stay in soils or other environments for thousands of years. This has given rise to the concept of biochar carbon removal, a process of carbon sequestration in the form of biochar. Carbon removal can be achieved when high-quality biochar is applied to soils, or added as a substitute material to construction materials such as concrete and tar.

Etymology

The word "biochar" is a late-20th century English neologism derived from the Greek word 'βίος' (bios, 'life') and 'char' (charcoal produced by carbonization of biomass). It is recognized as charcoal that participates in biological processes found in soil, aquatic habitats, and animal digestive systems.

History

Pre-Columbian Amazonians produced biochar by smoldering agricultural waste (i.e., covering burning biomass with soil) in pits or trenches. It is not known if they intentionally used biochar to enhance soil productivity. European settlers called it terra preta de Indio. Following observations and experiments, one research team working in French Guiana hypothesized that the Amazonian earthworm Pontoscolex corethrurus was the main agent of fine powdering and incorporation of charcoal debris in the mineral soil.

Production

Biochar is a high-carbon, fine-grained residue that is produced via pyrolysis. It is the direct thermal decomposition of biomass in the absence of oxygen, which prevents combustion, and produces a mixture of solids (biochar), liquid (bio-oil), and gas (syngas) products.

Gasification

Gasifiers produce most of the biochar sold in the United States. The gasification process consists of four main stages: oxidation, drying, pyrolysis, and reduction. Temperature during pyrolysis in gasifiers is 250–550 °C (523–823 K), 600–800 °C (873–1,073 K) in the reduction zone, and 800–1,000 °C (1,070–1,270 K) in the combustion zone.

The specific yield from pyrolysis (the step of gasification that produces biochar) is dependent on process conditions such as temperature, heating rate, and residence time. These parameters can be tuned to produce either more energy or more biochar. Temperatures of 400–500 °C (673–773 K) produce more char, whereas temperatures above 700 °C (973 K) favor the yield of liquid and gas fuel components. Pyrolysis occurs more quickly at higher temperatures, typically requiring seconds rather than hours. The increasing heating rate leads to a decrease in biochar yield, while the temperature is in the range of 350–600 °C (623–873 K). Typical yields are 60% bio-oil, 20% biochar, and 20% syngas. By comparison, slow pyrolysis can produce substantially more char (≈35%); this contributes to soil fertility. Once initialized, both processes produce net energy. For typical inputs, the energy required to run a "fast" pyrolyzer is approximately 15% of the energy that it outputs. Pyrolysis plants can use the syngas output and yield 3–9 times the amount of energy required to run.

The Amazonian pit/trench method, in contrast, harvests neither bio-oil nor syngas, and releases CO₂, black carbon, and other greenhouse gases (GHGs) (and potentially, toxicants) into the air, though less greenhouse gasses than captured during the growth of the biomass. Commercial-scale systems process agricultural waste, paper byproducts, and even municipal waste and typically eliminate these side effects by capturing and using the liquid and gas products. The 2018 winner of the X Prize Foundation for atmospheric water generators harvests potable water from the drying stage of the gasification process. The production of biochar as an output is not a priority in most cases.

Small-scale methods

Smallholder farmers in developing countries easily produce their own biochar without special equipment. They make piles of crop waste (e.g., maize stalks, rice straw, or wheat straw), light the piles on the top, and quench the embers with dirt or water to make biochar. This method greatly reduces smoke compared to traditional methods of burning crop waste. This method is known as the top-down burn or conservation burn.

Alternatively, more industrial methods can be used on small scales. While in a centralized system, unused biomass is brought to a central plant for processing into biochar, it is also possible for each farmer or group of farmers to operate a kiln. In this scenario, a truck equipped with a pyrolyzer moves from place to place to pyrolyze biomass. Vehicle power comes from the syngas stream, while the biochar remains on the farm. The biofuel is sent to a refinery or storage site. Factors that influence the choice of system type include the cost of transportation of the liquid and solid byproducts, the amount of material to be processed, and the ability to supply the power grid.

Various companies in North America, Australia, and England also sell biochar or biochar production units. In Sweden, the 'Stockholm Solution' is an urban tree planting system that uses 30% biochar to support urban forest growth. At the 2009 International Biochar Conference, a mobile pyrolysis unit with a specified intake of 1,000 pounds (450 kg) was introduced for agricultural applications.

Crops used

Common crops used for making biochar include various tree species, as well as various energy crops. Some of these energy crops (i.e. Napier grass) can store much more carbon on a shorter timespan than trees do.

For crops that are not exclusively for biochar production, the residue-to-product ratio (RPR) and the collection factor (CF), the percent of the residue not used for other things, measure the approximate amount of feedstock that can be obtained. For instance, Brazil harvests approximately 460 million tons (MT) of sugarcane annually, with an RPR of 0.30, and a CF of 0.70 for the sugarcane tops, which normally are burned in the field. This translates into approximately 100 MT of residue annually, which could be pyrolyzed to create energy and soil additives. Adding in the bagasse (sugarcane waste) (RPR=0.29, CF=1.0), which is otherwise burned (inefficiently) in boilers, raises the total to 230 MT of pyrolysis feedstock. Some plant residue, however, must remain on the soil to avoid increased costs and emissions from nitrogen fertilizers.

Hydrochar

Besides pyrolysis, torrefaction and hydrothermal carbonization processes can also thermally decompose biomass to the solid material. However, these products cannot be strictly defined as biochar. The carbon product from the torrefaction process contains some volatile organic components; thus its properties are between that of biomass feedstock and biochar. And although hydrothermal carbonization can produce a carbon-rich solid product, the process is evidently different from the conventional thermal conversion process, so the product is therefore defined as "hydrochar" rather than "biochar".

Thermo-catalytic depolymerization

Thermo-catalytic depolymerization is another method to produce biochar, which utilizes microwaves. It has been used to efficiently convert organic matter to biochar on an industrial scale, producing about 50% char.

Properties

A hand holding a piece of biochar with a bucket of it in the background — Biochar produced from residual wood

The physical and chemical properties of biochars as determined by feedstocks and technologies are crucial. Characterization data explain their performance in a specific use. For example, guidelines published by the International Biochar Initiative provide standardized evaluation methods. Properties can be categorized in several respects, including the proximate and elemental composition, pH value, and porosity. The atomic ratios of biochar, including H/C and O/C, correlate with the properties that are relevant to organic content, such as polarity and aromaticity. A van-Krevelen diagram can show the evolution of biochar atomic ratios in the production process. In the carbonization process, both the H/C and O/C atomic ratios decrease due to the release of functional groups that contain hydrogen and oxygen.

Production temperatures influence biochar properties in several ways. The molecular carbon structure of the solid biochar matrix is particularly affected. Initial pyrolysis at 450–550 °C leaves an amorphous carbon structure. Temperatures above this range will result in the progressive thermochemical conversion of amorphous carbon into turbostratic graphene sheets. Biochar conductivity also increases with production temperature. Important to carbon capture, aromaticity and intrinsic recalcitrance increases with temperature.

Applications

Carbon sink

The refractory stability of biochar leads to the concept of biochar carbon removal, a process of carbon sequestration in the form of biochar. It may be a means to mitigate climate change due to its potential of sequestering carbon with minimal effort. Biomass burning and natural decomposition releases large amounts of carbon dioxide and methane to the Earth's atmosphere. The biochar production process also releases CO₂ (up to 50% of the biomass); however, the remaining carbon content becomes indefinitely stable. Biochar carbon remains in the ground for centuries, slowing the growth in atmospheric greenhouse gas levels. Simultaneously, its presence in the earth can improve water quality, increase soil fertility, raise agricultural productivity, and reduce pressure on old-growth forests.

Biochar can sequester carbon in the soil for hundreds to thousands of years, like coal.According to the World Bank, "biochar retains between 10 percent and 70 percent (on average about 50 percent) of the carbon present in the original biomass and slows down the rate of carbon decomposition by one or two orders of magnitude, that is, in the scale of centuries or millennia". Early works proposing the use of biochar for carbon dioxide removal to create a long-term stable carbon sink were published in the early 2000s. This technique is advocated by scientists including James Hansen and James Lovelock.

A 2010 report estimated that sustainable use of biochar could reduce the global net emissions of carbon dioxide (CO
₂), methane, and nitrous oxide by up to 1.8 billion tonnes carbon dioxide equivalent (CO
₂e) per year (compared to the about 50 billion tonnes emitted in 2021), without endangering food security, habitats, or soil conservation. However a 2018 study doubted enough biomass would be available to achieve significant carbon sequestration. A 2021 review estimated potential CO₂ removal from 1.6 to 3.2 billion tonnes per year, and by 2023 it had become a lucrative business renovated by carbon credits.

As of 2023, the significance of biochar's potential as a carbon sink is widely accepted. Biochar was found to have the technical potential to sequester 7% of carbon dioxide on average across all countries, with twelve nations able to sequester over 20% of their greenhouse gas emissions—Bhutan leads this proportion (68%), followed by India (53%).

In 2021 the cost of biochar ranged around European carbon prices, but was not yet included in the EU or UK Emissions Trading Scheme.

Biochar adsorption of CO
₂ can be limited by the surface area of the material, which can be improved by using resonant acoustic mixing.

In developing countries, biochar derived from improved cookstoves for home-use can reduce carbon emissions (when the traditional cookstove is discontinued), as well as achieve other benefits for sustainable development.

Soil health

Biochar offers multiple soil health benefits in degraded tropical soils but is less beneficial in temperate regions. Its porous nature is effective at retaining both water and water-soluble nutrients. Soil biologist Elaine Ingham highlighted its suitability as a habitat for beneficial soil microorganisms. She pointed out that when pre-charged with these beneficial organisms, biochar promotes good soil and plant health.

Biochar reduces leaching of E-coli through sandy soils depending on application rate, feedstock, pyrolysis temperature, soil moisture content, soil texture, and surface properties of the bacteria.

For plants that require high potash and elevated pH, biochar can improve yield.

Biochar can improve water quality, reduce soil emissions of greenhouse gases, reduce nutrient leaching, reduce soil acidity, and reduce irrigation and fertilizer requirements. Due to its porosity, the small holes in biochar can keep water and dissolved minerals in the upper layers of soil, assisting plant growth and reducing the need for and expense of fertilizer. Under certain circumstances biochar induces plant systemic responses to foliar fungal diseases and improves plant responses to diseases caused by soilborne pathogens. Biochar can remove heavy metals from the soil.

Biochar's impacts are dependent on its properties as well as the amount applied, although knowledge about the important mechanisms and properties is limited. Biochar impact may depend on regional conditions including soil type, soil condition (depleted or healthy), temperature, and humidity. Modest additions of biochar reduce nitrous oxide (N
₂O) emissions by up to 80% and eliminate methane emissions, which are both more potent greenhouse gases than CO₂.

Studies reported positive effects from biochar on crop production in degraded and nutrient–poor soils. The application of compost and biochar under FP7 project FERTIPLUS had positive effects on soil humidity, crop productivity and quality in multiple countries. Biochar can be adapted with specific qualities to target distinct soil properties. In Colombian savanna soil, biochar reduced leaching of critical nutrients, created a higher nutrient uptake, and provided greater nutrient availability. At 10% levels, biochar reduced contaminant levels in plants by up to 80%, while reducing chlordane and DDX content in the plants by 68 and 79%, respectively. However, because of its high adsorption capacity, biochar may reduce pesticide efficacy. High-surface-area biochars may be particularly problematic.

Biochar may be plowed into soils in crop fields or added to gardens to enhance their fertility and stability and for medium- to long-term carbon sequestration in these soils. It even shows good results when top-dressed. It has shown positive effects in increasing soil fertility and improving disease resistance in West European soils. Gardeners taking individual action on climate change add biochar to soil, increasing plant yield and thereby drawing down more carbon. The use of biochar as a feed additive is a way to apply biochar to pastures and to reduce methane emissions.

Application rates of 2.5–20 tonnes per hectare (1.0–8.1 t/acre) appear required to improve plant yields significantly. Biochar costs in developed countries vary from $300–$7,000/tonne, which is generally impractical for the farmer/horticulturalist and prohibitive for low-input field crops. In developing countries, constraints on agricultural biochar relate more to biomass availability and production time. A compromise is to use small amounts of biochar in lower-cost biochar-fertilizer complexes.

Biochar soil amendments, when applied at excessive rates or with unsuitable soil type and biochar feedstock combinations, also have the potential for negative effects, including harming soil biota, reducing available water content, altering soil pH, and increasing salinity.

Slash-and-char

Switching from slash-and-burn to slash-and-char farming techniques in tropical regions can decrease both deforestation and carbon dioxide emission, as well as increase crop yields. Slash-and-burn leaves only 3% of the carbon from the organic material in the soil whereas slash-and-char can retain up to 50%. The global potential for carbon sequestration by shifting from slash-and-burn to slash-and-char farming techinques has been estimated to between 0.22 and 0.42 Gt C/yr. Biochar reduces the need for nitrogen fertilizers, thereby reducing cost and emissions from fertilizer production and transport. Additionally, by improving soil's till-ability, fertility, and productivity, biochar-enhanced soils can indefinitely sustain agricultural production. This is unlike slash-and-burn soils, which quickly become depleted of nutrients, forcing farmers to abandon fields, producing a continuous slash-and-burn cycle. Using pyrolysis to produce bio-energy does not require infrastructure changes the way, for example, processing biomass for cellulosic ethanol does. Additionally, biochar can be applied by the widely used machinery.

Water retention

Biochar is hygroscopic due to its porous structure and high specific surface area. As a result, fertilizer and other nutrients are retained for plants' benefit.

Stock fodder

Biochar has been used in animal feed for centuries.

Doug Pow, a Western Australian farmer, explored the use of biochar mixed with molasses as stock fodder. He asserted that in ruminants, biochar can assist digestion and reduce methane production. He also used dung beetles to work the resulting biochar-infused dung into the soil without using machinery. The nitrogen and carbon in the dung were both incorporated into the soil rather than staying on the soil surface, reducing the production of nitrous oxide and carbon dioxide. The nitrogen and carbon added to soil fertility. On-farm evidence indicates that the fodder led to improvements of liveweight gain in Angus-cross cattle. Doug Pow won the Australian Government Innovation in Agriculture Land Management Award at the 2019 Western Australian Landcare Awards for this innovation.Pow's work led to two further trials on dairy cattle, yielding reduced odour and increased milk production.

Concrete additive

Ordinary Portland cement (OPC), an essential component of concrete mix, is energy- and emissions-intensive to produce; cement production accounts for around 8% of global CO₂ emissions. The concrete industry has increasingly shifted to using supplementary cementitious materials (SCMs), additives that reduce the volume of OPC in a mix while maintaining or improving concrete properties. Biochar has been shown to be an effective SCM, reducing concrete production emissions while maintaining required strength and ductility properties.

Studies have found that a 1–2% weight concentration of biochar is optimal for use in concrete mixes, from both a cost and strength standpoint. A 2 wt.% biochar solution has been shown to increase concrete flexural strength by 15% in a three-point bending test conducted after 7 days, compared to traditional OPC concrete. Biochar concrete also shows promise in high-temperature resistance and permeability reduction.

A cradle-to-gate life cycle assessment of biochar concrete showed decreased production emissions with higher concentrations of biochar, which tracks with a reduction in OPC. Compared to other SCMs from industrial waste streams (such as fly ash and silica fume), biochar also showed decreased toxicity.

Fuel slurry

Biochar mixed with liquid media such as water or organic liquids (such as ethanol) is an emerging fuel type known as biochar-based slurry. Adapting slow pyrolysis in large biomass fields and installations enables the generation of biochar slurries with unique characteristics. These slurries are becoming promising fuels in countries with regional areas where biomass is abundant, and power supply relies heavily on diesel generators. This type of fuel resembles a coal slurry, but with the advantage that it can be derived from biochar from renewable resources.

Water treatment

Biochar also has applications in water treatment. Its properties, porosity in particular, can be modified using different methods to increase the efficiency of contaminant removal. Biochar is reported to remove contaminants such as heavy metals, dyes, organic pollutants.

Research

Research into pyrolysis and biochar is underway globally, but as of 2018 was still in its infancy. From 2005 to 2012, 1,038 articles included the word "biochar" or "bio-char" in the topic indexed in the ISI Web of Science. Research is in progress by the University of Edinburgh, the University of Georgia the Volcani Center, and the Swedish University of Agricultural Sciences.

Research is also ongoing on the application of biochar to coarse soils in semi-arid and degraded ecosystems. In Namibia, biochar is under exploration as a climate change adaptation effort, strengthening local communities' drought resilience and food security through the local production and application of biochar from abundant encroacher biomass. Similar solutions for rangeland affected by woody plant encroachment have been explored in Australia.

In recent years, biochar has attracted interest as a wastewater filtration medium as well as for its adsorbing capacity for wastewater pollutants, such as pharmaceuticals, personal care products, and per- and polyfluoroalkyl substances.

In some areas, citizen interest and support for biochar motivates government research into the uses of biochar.

Studies

Long-term effects of biochar on carbon sequestration have been examined using soil from arable fields in Belgium with charcoal-enriched black spots dating from before 1870 from charcoal production mound kilns. This study showed that soil treated over a long period with charcoal showed a higher proportion of maize-derived carbon and decreased respiration, attributed to physical protection, carbon saturation of microbial communities, and, potentially, slightly higher annual primary production. Overall, this study evidences the capacity of biochar to enhance carbon sequestration through reduced carbon turnover.

Biochar sequesters carbon in soils because of its prolonged residence time, ranging from years to millennia. In addition, biochar can promote indirect carbon sequestration by increasing crop yield while potentially reducing carbon mineralization. Laboratory studies have evidenced effects of biochar on carbon mineralization using ¹³
C signatures.

Fluorescence analysis of organic matter dissolved in biochar-amended soil revealed that biochar application increased a humic-like fluorescent component, likely associated with biochar-carbon in solution. The combined spectroscopy-microscopy approach revealed the accumulation of aromatic carbon in discrete spots in the solid phase of microaggregates and its co-localization with clay minerals for soil amended with raw residue or biochar. Biochar application consistently reduced the co-localization of aromatic carbon and polysaccharides carbon. These findings suggested that reduced carbon metabolism is an important mechanism for carbon stabilization in biochar-amended soils.

Similarity measure

From Wikipedia, the free encyclopedia

In statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects. Although no single definition of a similarity exists, usually such measures are in some sense the inverse of distance metrics: they take on large values for similar objects and either zero or a negative value for very dissimilar objects. Though, in more broad terms, a similarity function may also satisfy metric axioms.

Cosine similarity is a commonly used similarity measure for real-valued vectors, used in (among other fields) information retrieval to score the similarity of documents in the vector space model. In machine learning, common kernel functions such as the RBF kernel can be viewed as similarity functions.

Use of different similarity measure formulas

Different types of similarity measures exist for various types of objects, depending on the objects being compared. For each type of object there are various similarity measurement formulas.

Similarity between two data points

There are many various options available when it comes to finding similarity between two data points, some of which are a combination of other similarity methods. Some of the methods for similarity measures between two data points include Euclidean distance, Manhattan distance, Minkowski distance, and Chebyshev distance. The Euclidean distance formula is used to find the distance between two points on a plane, which is visualized in the image below. Manhattan distance is commonly used in GPS applications, as it can be used to find the shortest route between two addresses. When you generalize the Euclidean distance formula and Manhattan distance formula you are left with the Minkowski distance formulas, which can be used in a wide variety of applications.

Similarity between strings

For comparing strings, there are various measures of string similarity that can be used. Some of these methods include edit distance, Levenshtein distance, Hamming distance, and Jaro distance. The best-fit formula is dependent on the requirements of the application. For example, edit distance is frequently used for natural language processing applications and features, such as spell-checking. Jaro distance is commonly used in record linkage to compare first and last names to other sources.

Similarity between two probability distributions

Typical measures of similarity for probability distributions are the Bhattacharyya distance and the Hellinger distance. Both provide a quantification of similarity for two probability distributions on the same domain, and they are mathematically closely linked. The Bhattacharyya distance does not fulfill the triangle inequality, meaning it does not form a metric. The Hellinger distance does form a metric on the space of probability distributions.

Similarity between two sets

The Jaccard index formula measures the similarity between two sets based on the number of items that are present in both sets relative to the total number of items. It is commonly used in recommendation systems and social media analysis. The Sørensen–Dice coefficient also compares the number of items in both sets to the total number of items present but the weight for the number of shared items is larger. The Sørensen–Dice coefficient is commonly used in biology applications, measuring the similarity between two sets of genes or species.

Similarity between two sequences

When comparing temporal sequences (time series), some similarity measures must additionally account for similarity of two sequences that are not fully aligned.

Dynamic time warping

Use in clustering

Clustering or Cluster analysis is a data mining technique that is used to discover patterns in data by grouping similar objects together. It involves partitioning a set of data points into groups or clusters based on their similarities. One of the fundamental aspects of clustering is how to measure similarity between data points.

Similarity measures play a crucial role in many clustering techniques, as they are used to determine how closely related two data points are and whether they should be grouped together in the same cluster. A similarity measure can take many different forms depending on the type of data being clustered and the specific problem being solved.

One of the most commonly used similarity measures is the Euclidean distance, which is used in many clustering techniques including K-means clustering and Hierarchical clustering. The Euclidean distance is a measure of the straight-line distance between two points in a high-dimensional space. It is calculated as the square root of the sum of the squared differences between the corresponding coordinates of the two points. For example, if we have two data points $(x_{1}, y_{1})$ and $(x_{2}, y_{2})$ , the Euclidean distance between them is $d = \sqrt [(x_{2} - x_{1})^{2} + (y_{2} - y_{1})^{2}]$ .

Another commonly used similarity measure is the Jaccard index or Jaccard similarity, which is used in clustering techniques that work with binary data such as presence/absence data or Boolean data; The Jaccard similarity is particularly useful for clustering techniques that work with text data, where it can be used to identify clusters of similar documents based on their shared features or keywords. It is calculated as the size of the intersection of two sets divided by the size of the union of the two sets: $J (A, B) = \frac{A ⋂ B}{A ⋃ B}$ .

Similarities among 162 Relevant Nuclear Profile are tested using the Jaccard Similarity measure (see figure with heatmap). The Jaccard similarity of the nuclear profile ranges from 0 to 1, with 0 indicating no similarity between the two sets and 1 indicating perfect similarity with the aim of clustering the most similar nuclear profile.

Manhattan distance, also known as Taxicab geometry, is a commonly used similarity measure in clustering techniques that work with continuous data. It is a measure of the distance between two data points in a high-dimensional space, calculated as the sum of the absolute differences between the corresponding coordinates of the two points $| x_{1} - x_{2} | + | y_{1} - y_{2} |$ .

When dealing with mixed-type data, including nominal, ordinal, and numerical attributes per object, Gower's distance (or similarity) is a common choice as it can handle different types of variables implicitly. It first computes similarities between the pair of variables in each object, and then combines those similarities to a single weighted average per object-pair. As such, for two objects $i$ and $j$ having $p$ descriptors, the similarity $S$ is defined as: $S_{i j} = \frac{\sum_{k = 1}^{p} w_{i j k} s_{i j k}}{\sum_{k = 1}^{p} w_{i j k}},$ where the $w_{i j k}$ are non-negative weights and $s_{i j k}$ is the similarity between the two objects regarding their $k$ -th variable.

In spectral clustering, a similarity, or affinity, measure is used to transform data to overcome difficulties related to lack of convexity in the shape of the data distribution. The measure gives rise to an $(n, n)$ -sized similarity matrix for a set of $n$ points, where the entry $(i, j)$ in the matrix can be simply the (reciprocal of the) Euclidean distance between $i$ and $j$ , or it can be a more complex measure of distance such as the Gaussian $e^{- ‖ s_{1} - s_{2} ‖^{2} / 2 σ^{2}}$ . Further modifying this result with network analysis techniques is also common.

The choice of similarity measure depends on the type of data being clustered and the specific problem being solved. For example, working with continuous data such as gene expression data, the Euclidean distance or cosine similarity may be appropriate. If working with binary data such as the presence of a genomic loci in a nuclear profile, the Jaccard index may be more appropriate. Lastly, working with data that is arranged in a grid or lattice structure, such as image or signal processing data, the Manhattan distance is particularly useful for the clustering.

Use in recommender systems

Similarity measures are used to develop recommender systems. It observes a user's perception and liking of multiple items. On recommender systems, the method is using a distance calculation such as Euclidean Distance or Cosine Similarity to generate a similarity matrix with values representing the similarity of any pair of targets. Then, by analyzing and comparing the values in the matrix, it is possible to match two targets to a user's preference or link users based on their marks. In this system, it is relevant to observe the value itself and the absolute distance between two values. Gathering this data can indicate a mark's likeliness to a user as well as how mutually closely two marks are either rejected or accepted. It is possible then to recommend to a user targets with high similarity to the user's likes.

Recommender systems are observed in multiple online entertainment platforms, in social media and streaming websites. The logic for the construction of this systems is based on similarity measures.

Use in sequence alignment

Similarity matrices are used in sequence alignment. Higher scores are given to more-similar characters, and lower or negative scores for dissimilar characters.

Nucleotide similarity matrices are used to align nucleic acid sequences. Because there are only four nucleotides commonly found in DNA (Adenine (A), Cytosine (C), Guanine (G) and Thymine (T)), nucleotide similarity matrices are much simpler than protein similarity matrices. For example, a simple matrix will assign identical bases a score of +1 and non-identical bases a score of −1. A more complicated matrix would give a higher score to transitions (changes from a pyrimidine such as C or T to another pyrimidine, or from a purine such as A or G to another purine) than to transversions (from a pyrimidine to a purine or vice versa). The match/mismatch ratio of the matrix sets the target evolutionary distance. The +1/−3 DNA matrix used by BLASTN is best suited for finding matches between sequences that are 99% identical; a +1/−1 (or +4/−4) matrix is much more suited to sequences with about 70% similarity. Matrices for lower similarity sequences require longer sequence alignments.

Amino acid similarity matrices are more complicated, because there are 20 amino acids coded for by the genetic code, and so a larger number of possible substitutions. Therefore, the similarity matrix for amino acids contains 400 entries (although it is usually symmetric). The first approach scored all amino acid changes equally. A later refinement was to determine amino acid similarities based on how many base changes were required to change a codon to code for that amino acid. This model is better, but it doesn't take into account the selective pressure of amino acid changes. Better models took into account the chemical properties of amino acids.

One approach has been to empirically generate the similarity matrices. The Dayhoff method used phylogenetic trees and sequences taken from species on the tree. This approach has given rise to the PAM series of matrices. PAM matrices are labelled based on how many nucleotide changes have occurred, per 100 amino acids. While the PAM matrices benefit from having a well understood evolutionary model, they are most useful at short evolutionary distances (PAM10–PAM120). At long evolutionary distances, for example PAM250 or 20% identity, it has been shown that the BLOSUM matrices are much more effective.

The BLOSUM series were generated by comparing a number of divergent sequences. The BLOSUM series are labeled based on how much entropy remains unmutated between all sequences, so a lower BLOSUM number corresponds to a higher PAM number.

Use in computer vision

The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others. For example, a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images. Search results then can be sorted based on their distance to the queried image. Many measures of image distance (Similarity Models) have been developed.

Search This Blog