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Soot

Soot is a fine, black carbonaceous partical matter produced during the incomplete combustion of various organic materials, such as coal, oil, wood, and other fuels.
It is a major component of particulate air pollution and can have adverse effects on human health and the environment.
Soot analysis is an important tool for understanding the sources, composition, and impacts of this ubiquitous pollutant.
PubCompare.ai's AI-driven platform can help streamline and optimize soot analysis research protocols, enhancing reproducibility and enabling researchers to easily locate relevant protocols from literature, pre-prints, and patents.
The platform's powerful comparison tools can help identify the best protocols and products for specific research needs, taking soot analysis to the next leve1.

Most cited protocols related to «Soot»

1
Surface Modification of Carbon Black Nanoparticles
Cited 6 times
To understand how surface modification can change the interaction of CBNP with biological systems we used PAHs to modify the surface of a toxicologically well-defined CBNP, Printex®90 (P90). This is characterized by a high surface area and has been widely used in toxicological studies, resulting in only minor toxic effects [1 (link), 33 (link), 34 (link)].
For modification of the P90 surface, we used benzo[a]pyrene (BaP) and 9-nitroanthracene (9NA). BaP was chosen because of the well characterized toxicity of its metabolites, which are known to induce ROS and DNA adducts [23 (link), 35 (link)–38 (link)]. BaP is known to induce Cyp1A1 and 1B1, which then metabolize BaP to toxic metabolites; this therefore allows monitoring of BaP activity and its biological effect [39 (link), 40 (link)]. In contrast, 9NA is a PAH that occurs during combustion, and is regarded as a low toxicity PAH, as predicted by the Ames test and human cell mutagenicity assay [41 (link)–43 (link)]. However, due to its nitro group other toxic mechanisms can occur induced by intermediates resulting from reduction of the nitro group [41 (link)]. As coating of a particle does not necessarily represent the situation found in nanoparticles that acquire PAH during synthesis, we also generated CBNP by acetylene combustion [44 ]. The resulting acetylene soot (AS) had a mixture of PAHs on the surface (AS-PAH). In the suspensions we used, AS-PAH had a slightly larger specific surface area, but similar aggregate size and ζ-potential compared to PAH-coated P90, The physicochemical parameters of the different particles were evaluated by a variety of analytic test methods (see Tables 1, 2 in the Results section and Additional files 1, 2).
Lindner K., Ströbele M., Schlick S., Webering S., Jenckel A., Kopf J., Danov O., Sewald K., Buj C., Creutzenberg O., Tillmann T., Pohlmann G., Ernst H., Ziemann C., Hüttmann G., Heine H., Bockhorn H., Hansen T., König P, & Fehrenbach H. (2017). Biological effects of carbon black nanoparticles are changed by surface coating with polycyclic aromatic hydrocarbons. Particle and Fibre Toxicology, 14, 8.
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Publication 2017
9-nitroanthracene Acetylene Anabolism Benzo(a)pyrene Biological Assay Biopharmaceuticals Cells Cytochrome P-450 CYP1A1 DNA Adducts Mineralocorticoid Excess Syndrome, Apparent Mutagens Polycyclic Hydrocarbons, Aromatic Soot
2
Estimating Exposure to Air Pollution
Cited 5 times
Exposure to air pollution in each area was estimated following a standard methods developed for the ESCAPE project and described elsewhere.28
29 Briefly, for each area under study, particulate matter of varying sizes measured in μm (shown as subscript)—that is, PM10, coarse PM, PM2.5, and PM2.5 absorbance (blackness of the PM2.5 exposed filter, determined by measurement of light reflectance as a marker for soot and black carbon)—was measured in 20 sites, and nitrogen oxides were measured in 40 sites in three separate two week periods (to cover different seasons) over one year (between 2008 and 2011). For each site, results from the three measurements were averaged to estimate the annual average, with adjustment for temporal variation by using a centrally located background reference site, which was operated for a whole year.30 (link)
31 By using several traffic and land use variables, we developed area specific land use regression (LUR) models to explain the spatial variation of each measured pollutant. These models were then used to estimate concentrations of air pollution at each participant’s residential address. Geographical variables typically evaluated include altitude, population density, industrial land use, green space, and traffic flows variables.30 (link)
31 In addition to concentrations of pollutants, we considered two traffic variables at the participant’s residence: traffic intensity on the nearest road (vehicles/day) and traffic load on major roads in a 100 m buffer (vehicles×m/day), defined as the sum of traffic intensity on roads with >5000 vehicles/day multiplied by the length of those roads in a 100 m buffer. To validate the models, we used the leave one out cross validation method— that is, systematically subtracting each of the monitoring points from the model one by one, and then comparing the predicted value for each monitoring location with the measured level at the location without using this measurement in the development of the model.30 (link)
31
Cesaroni G., Forastiere F., Stafoggia M., Andersen Z.J., Badaloni C., Beelen R., Caracciolo B., de Faire U., Erbel R., Eriksen K.T., Fratiglioni L., Galassi C., Hampel R., Heier M., Hennig F., Hilding A., Hoffmann B., Houthuijs D., Jöckel K.H., Korek M., Lanki T., Leander K., Magnusson P.K., Migliore E., Ostenson C.G., Overvad K., Pedersen N.L., J J.P., Penell J., Pershagen G., Pyko A., Raaschou-Nielsen O., Ranzi A., Ricceri F., Sacerdote C., Salomaa V., Swart W., Turunen A.W., Vineis P., Weinmayr G., Wolf K., de Hoogh K., Hoek G., Brunekreef B, & Peters A. (2014). Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project. The BMJ, 348, f7412.
Publication 2014
Air Pollution Buffers Carbon Black Environmental Pollutants Light Negroes Nitrogen Oxides Soot
3
Comprehensive Index of Environmental Regulation Intensity
Cited 4 times
For the measurement of environmental regulation, various methods are employed in the existing literature, but, at present, the most popular one is to adopt the comprehensive index of environmental regulation intensity. This involves taking the emission intensity of the different pollutants of an industry as the measure of the stringency of environmental regulation. Nevertheless, this method has two likely issues. First, due to the inconsistency of the main pollutants across industries, the indicators of different pollutant discharges need a weighted average adjustment. Second, considering the different attributes and characteristics among industries, different industries need to make different degrees of effort to meet the same environmental requirements for emissions. In order to solve the two problems above, we refer to the method of Wang et al. [41 ]. In addition, to construct the comprehensive index of the intensity of environmental regulation, allowing for the availability of data on pollutant discharges, this study selects the rate of compliance for industrial wastewater discharge standards, the removal rate of industrial SO2, the removal rate of industrial soot, the removal rate of industrial dust, and the ratio of industrial solid waste utilized.
First, each individual indicator of pollutant discharge is standardized as follows: PEsht=(Esht−Min Eh)/(Max Eh−Min Eh),
where Esht is the original value of the indicator for pollutant h; MaxEh and MinEh represent the maximum and minimum values, respectively, of the indicator for pollutant h over the sample period; and PEsht is the standardized value of the indicator for pollutant h.
Second, we take a weighted average of the five indicators of pollutant discharge above. As the discharge of pollutants in different industries varies greatly, the weights are determined by: Wsht=Esht∑Esht/Ost∑Ost,
where Ost is the total industrial output, and Wsht denotes the weight. Equation (18) indicates that the weight of pollutant h in industry s is determined by the relative emission intensity of pollutant h. Thus, it is beneficial for eliminating the impact of pollution emission intensity differences between industries.
Third, we use the operating expenses of pollution control facilities per unit of output as the measure of the pollution treatment costs ( feest ) of industries to deal with the discrepancy in effort expended between industries with different pollution intensity aiming to meet the same emission standard. The weight of pollution control costs in each industry is given by: wfeest=feest/∑feest,
Finally, the intensity of environmental regulation can be measured by: ERst=wfeest×1N∑Wsht×PEsht,
where N represents the number of industries.
Xie J., Sun Q., Wang S., Li X, & Fan F. (2020). Does Environmental Regulation Affect Export Quality? Theory and Evidence from China. International Journal of Environmental Research and Public Health, 17(21), 8237.
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Publication 2020
Environmental Pollutants Industrial Waste Patient Discharge Soot
4
Evaluating microAeth for Fixed-site BC Monitoring
Cited 4 times
To evaluate and validate microAeth for use as fixed site monitor, side-by-side testing of several microAeth units and comparisons of the microAeth units with other established BC measurement methods, including a rack-mount Aethalometer and multi-wavelength optical measurements on integrated Teflon filters, were conducted. Of particular note the multi-wavelength optical measurement of BC is calibrated gravimetrically by collecting, weighing and then optically measuring PM2.5 filters of kerosene soot and assuming that 100% of the mass is BC (Yan et al., 2011 ); literature values consistently show values of 95% ± 5% (Lam et al., 2012 (link)). This comparison testing was conducted from the window of a fifth floor apartment along the W168th street, NYC, which is situated at an intersection of a busy street with idling ambulances and a truck route with heavy traffic. However, the sampling window faced a courtyard rather than the street. Six microAeth units were inside the apartment; sampling tubing passed through a window board, and the sampling inlets were about 0.3 m from the outside wall. MicroAeths were set to acquire BC data for every 1 min at a flow rate of 100 mL/min and replace the filter for every 24 hrs.
In a separate experiment, four of six microAeth units were operated in turn along with a rack-mount Aethalometer® (AE22, Magee Scientific Co.) continuously measuring BC level every minute at the flow rate of 4.0 LPM and three integrated PM2.5 samplers (KTL cyclones BGI, Inc.) collecting PM2.5 on 37 mm Teflon membrane filters (Pall Corporation, Port Washington, NY) at flow rate of 4.0 LPM. This comparison test ran for four weeks with three consecutive 24-hr sampling periods every week. The full size Aethalometer used a web-reinforced quartz fiber filter tape (supplied by Magee Science Co.), allowing an automatic advance of the filter when the sampling spot became heavily loaded.
A multi-wavelength optical method has been established for measuring BC levels collected on PM2.5 Teflon filters via optical equipment purchased from Ocean Optics (Dunedin, FL) including a balanced deuterium tungsten halogen light source (DH-2000-BAL), an integrating sphere (ISP-50-8-R) modified to have a reflective white bottom, a lab-made filter holder, and an Ocean Optics USB4000-VIS-NIR fiber-optic spectrometer (Yan et al., 2011 ). Whereas the microAeth and full size Aethalometer measures the change in light attenuation between every time stamp (e.g., each min), from which the BC air concentration is calculated for that period of time, the multi-wavelength optical reflectance method determines the total BC mass loading on filters (based on an empirical gravimetric calibration) that occurred over the entire deployment period, which was 24 hrs in this experiment. For comparison, data retrieved from both microAeth and full size Aethalometer in the 24 hours period were integrated and the daily average BC level were compared to those measured by the multi-wavelength method. The high resolution temporal data from both microAeth and Aethalometer was compared with each other; data from a single random day was selected to better illustrate details.
Cai J., Yan B., Ross J., Zhang D., Kinney P.L., Perzanowski M.S., Jung K., Miller R, & Chillrud S.N. (2014). Validation of MicroAeth® as a Black Carbon Monitor for Fixed-Site Measurement and Optimization for Personal Exposure Characterization. Aerosol and air quality research, 14(1), 1-9.
Publication 2014
Ambulances Cyclonic Storms Deuterium Eye Fibrosis Halogens Kerosene Light Quartz Soot Teflon Tissue, Membrane Tungsten Vision
5
Wildfire Soot Aerosol Characterization
Cited 4 times
The Las Conchas wildfire started on 26 June 2011 in northern New Mexico, USA, and burned an area of 245 square miles. This was the second largest wildfire in New Mexico state history and the largest at the time. The particles were collected during the smouldering phase at a mean distance of ~25 km from the emission location (~1–2 h aged). Aerosol samples were collected on nucleopore filters (100 nm pores) at the Physics Building in Los Alamos National Laboratory during the third week of the wildfire. A thermodenuder (University of Northwest, Switzerland) was used to remove volatile compounds at temperatures up to 200 °C, leaving behind refractory soot and low-volatility compounds. We note that even at 200°C many non-refractory organic material and other inorganic coatings may still remain11 . Two sample lines were used for collecting ambient and denuded particles with automated switching occurring at 5 min intervals. Two sets of ambient and denuded samples were collected on 12 July; the first set (ambient-1 and denuded-1) was collected from 1300 to 1720 hours, and the second set (ambient-2 and denuded-2) was collected from 1730 to 1800 hours. The denuding system was originally set up also to study the optical properties of the aerosol. To study the changes in optical properties versus the denuding temperature, the temperature during the sample-1 period was ramping from 100 to 200 °C with a mean time-weighted temperature of 152 °C. The analysis and discussion of the optical properties of the aerosols are beyond the scope of this paper, as we intentionally focus on the morphological properties. However, to accumulate enough samples for electron microscopy, aerosol was collected during the entire period and we cannot separate particles that underwent different denuding temperatures, although we can still obtain useful information on a statistical basis. On the contrary, for the denuded-2 (D-2) sample, the denuder temperature was kept constant at 200 °C.
China S., Mazzoleni C., Gorkowski K., Aiken A.C, & Dubey M.K. (2013). Morphology and mixing state of individual freshly emitted wildfire carbonaceous particles. Nature Communications, 4, 2122.
Publication 2013
Electron Microscopy Nuclear Pore Soot Vision Volatility Wildfires

Most recents protocols related to «Soot»

1
Comprehensive Characterization Methods
The characterization methods of X-ray diffraction (XRD), N2 adsorption–desorption, scanning electron microscopy (SEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), H2 temperature-programmed reduction (H2-TPR) and O2 temperature-programmed desorption (O2-TPD), Soot temperature programmed reduction (Soot-TPR) and in situ IR spectra are described in the Supporting information.
Zhu Y., Chen Z., Li H., Wang Q., Liu X., Hu Y., Su C., Duan R., Chen S, & Lan L. (2023). Effect of oxygen vacancy and highly dispersed MnOx on soot combustion in cerium manganese catalyst. Scientific Reports, 13, 3386.
Full text: Click here
Publication 2023
Adsorption Infrared Spectrophotometry Scanning Electron Microscopy Soot Spectrum Analysis, Raman X-Ray Diffraction
2
Soot Combustion Catalysis by CeMn
The catalytic activity of cerium manganese catalyst for soot combustion was measured by means of TGA/DSC thermogravimetric analyzer (METTLER, Swiss) with Printex-U (Degussa, Germany) used as the model of diesel soot. The soot and the catalyst (weight ratio was 1:10) were carefully ground in a mortar for 10 min to achieve the “tight contact” condition. In order to make the evaluation condition similar to the actual condition, the catalyst and soot were mixed with shovel for 5 min to achieve the “loose contact” condition. The reaction test was carried out with 10% O2/N2 which was close to the oxygen concentration in diesel exhaust. Activity tests were carried out from 30 to 600 °C under a gas flow rate of 30 ml/min, and the heating rate was maintained at 10 °C/min. Tm represents the temperature corresponding to the maximum heat release during soot combustion in the DSC diagram and the peak value of the DTG curve (They're the same). The lower the Tm, the easier the combustion of soot and the better the activity of the catalyst. TPO-MS was used to detect the CO2, CO and H2O produced during the heating process (Supporting information).
Zhu Y., Chen Z., Li H., Wang Q., Liu X., Hu Y., Su C., Duan R., Chen S, & Lan L. (2023). Effect of oxygen vacancy and highly dispersed MnOx on soot combustion in cerium manganese catalyst. Scientific Reports, 13, 3386.
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Publication 2023
Cerium Diesel Exhaust enzyme activity Manganese Oxygen Soot
3
Outdoor Air Pollution and Birth Outcomes
Cited 2 times
Baseline characteristics were tabulated to describe general characteristics of the mothers and infants, as well as for smoking habits and other environmental factors such as home fuel resources usage, garbage burning, and household insecticides usage. Continuous variables were expressed as mean and standard deviation or median and interquartile range if distributions were skewed. Categorical variables were expressed as number of subject and its percentage.
Socio-economic status (SES) i.e. household income and level of education, maternal age, parity [6 (link), 18 (link), 19 (link)], BMI increment during pregnancy (Δ BMI) [19 (link)], mother’s working status, mother’s active and passive smoking during pregnancy [8 (link), 19 (link)], and pesticides exposure in pregnancy were a priori considered as possible confounders. All of the possible confounders were treated as categorical, excluding mother’s age and Δ BMI. Level of education was categorized as elementary, high school, and under-post graduate, while family income was categorized as below the minimum monthly wedges per capita in Jakarta (< 290 USD) or above or equal to the minimum monthly wedges per capita in Jakarta (≥ 290 USD). Maternal co-morbidity/gestational complication [20 (link)] and infant gender were considered as possible effect modifiers [18 (link), 19 (link)].
Multiple linear regression models adjusted for gestational age and subsequently all potential covariates were specified to evaluate the association between outdoor concentration of PM2.5, soot, NOx, and NO2 with birth weight and birth length. Logistic regression was used to explore the association between outdoor air pollutants concentration with low birth weight, expressed as OR with 95% confidence interval. We performed sensitivity analysis to test the robustness of our results when restricting the cohort to women with infant born at gestational age ≥ 37 weeks. We additionally calculated the difference between PM2.5 and soot to further differentiate effects of PM2.5 and soot. This difference was highly correlated (R = 0.99) with PM2.5. We also isolated the effect of soot from PM2.5 by generating new variable as the residual of soot [21 (link)] and further, we tested the association of this new variable (the residual of soot) with birth anthropometry. All effect estimates were expressed for an interquartile increase of each air pollutant i.e. 7.14 μg/m3 for PM2.5, 0.75 × 10− 5 per m for soot, 4.68 μg/m3 for NOx, and 3.74 μg/m3 for NO2. Statistical significance was assumed if 95% confidence intervals did not include the estimation of null values, corresponding to two-sided p values < 0.05. Statistical analyses were conducted using IBM SPSS version 24 for Mac, while the LUR models were developed using R 4.1.2.
Soesanti F., Uiterwaal C.S., Meliefste K., Chen J., Brunekreef B., Idris N.S., Grobbee D.E., Klipstein-Grobusch K, & Hoek G. (2023). The effect of exposure to traffic related air pollutants in pregnancy on birth anthropometry: a cohort study in a heavily polluted low-middle income country. Environmental Health, 22, 22.
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Publication 2023
Air Pollutants Birth Birth Weight Childbirth Garbage Gestational Age Head Households Hypersensitivity Infant Insecticides Mothers Pesticides Pregnancy Pregnancy Complications Soot Woman
4
Assessing Air Pollution Exposure in Jakarta
Cited 1 time
Exposure assessment for the cohort was based upon land use regression (LUR) models [14 (link)] developed based on targeted measurements of fine particles and nitrogen oxides in Jakarta. The study area was defined by the primary care catchment area of the cohort study. The study area is part of the center of Jakarta (Fig. 1). LUR models were developed for particles smaller than 2.5 μm (PM2.5), soot (a measure of black carbon), nitrogen dioxide (NO2) and the sum of nitrogen dioxide (NO2) and nitrogen oxide (NO), denoted as NOx. Measurements were made at 88 sites across the study area. We selected 37 urban background sites, 7 urban green sites and 44 traffic sites, based upon the assumption that motorized traffic is an important source of spatial variability in the study area. Annual average concentrations were calculated after correction for temporal variation with continuous measurements from a reference site [14 (link)].

Study area of Jakarta air pollution sampling. Red pin indicates traffic sites, yellow pin: urban background, green pin: urban green, white pin indicates reference site

LUR models were developed using predictor variables obtained from direct systematic field observations of traffic counts and street configuration and global GIS databases of road data from Open Street map and impervious surface (Refer to Additional file 1). Models were developed using supervised linear regression procedures, extensively used in previous LUR studies [15 (link)]. The developed LUR models are listed in Supplemental Table 1. All models included motorcycle counts at the nearest road as an important predictor variable. The LUR models explained 61, 59, 26 and 33% of the measured annual average concentration variability for NOx, NO2, PM2.5 and soot respectively. The models thus explain a moderate amount of the measured variability. Exposure to air pollution was assessed at individual level, calculated for each member of the cohort by applying the LUR model to the residential address [16 (link)].
Because of the lack of continuous monitoring data for all four evaluated pollutants, we could not use extrapolation methods. Continuous monitoring data was available from the US embassy only for PM2.5 [17 ]. We downloaded hourly data from 2016 to 2020 for the central Jakarta site and calculated annual average to evaluate a possible trend over time. We further evaluated variability of monthly averages and calculated full pregnancy average concentrations related to temporal variation. We also measured indoor air pollutants concentration for PM2.5, soot, NOx, NO2 in a subset of 47 randomly selected participants (Refer to Additional file 1).
Soesanti F., Uiterwaal C.S., Meliefste K., Chen J., Brunekreef B., Idris N.S., Grobbee D.E., Klipstein-Grobusch K, & Hoek G. (2023). The effect of exposure to traffic related air pollutants in pregnancy on birth anthropometry: a cohort study in a heavily polluted low-middle income country. Environmental Health, 22, 22.
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Publication 2023
Air Pollutants Air Pollution Carbon Black Environmental Pollutants Nitrogen Dioxide Nitrogen Oxides Pregnancy Primary Health Care Soot
5
Sooty Tern Optimization for Cancer Nodule Partitioning
The process of feature optimization achieved using inspiration derived from the sooty tern optimization algorithm (SHOA) is detailed below [28 ]. It is mainly adopted for partitioning the nodules of cancer by defining the best characteristics, which aids in improving diagnostic accuracy. The implementation of SHOA towards feature optimization is achieved using the phases of migration and attack, representing exploration and exploitation, respectively. Algorithm 3 provides the working of SHOA for feature optimization, and Figure 4 provides the flow chart of STOA algorithm.
Saleem M.A., Thien Le N., Asdornwised W., Chaitusaney S., Javeed A, & Benjapolakul W. (2023). Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours. Sensors (Basel, Switzerland), 23(4), 2147.
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Publication 2023
Acquired Immunodeficiency Syndrome Diagnosis Inhalation Malignant Neoplasms Soot Terns

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Buckyprep m by Nacalai Tesque
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Buckyprep-M is a laboratory equipment used for the purification of carbon nanotubes. It utilizes a proprietary adsorption technique to selectively separate and concentrate carbon nanotubes from complex mixtures.
3
Buckyprep d by Nacalai Tesque
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Buckyprep-D is a laboratory equipment designed for the purification of carbon nanotubes. It utilizes a proprietary separation technique to effectively isolate and concentrate single-walled carbon nanotubes from complex mixtures.
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Printex u by Evonik
Printex-U is a carbon black product manufactured by Evonik. It is a fine powder with a high purity and specific surface area, designed for use as a laboratory and research material.
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2200fs microscope by JEOL
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Combusted argon carrier gas by Merck Group
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More about "Soot"

Soot, a ubiquitous black carbon particulate, is a product of incomplete combustion processes involving various organic materials such as coal, oil, wood, and other fuels.
This fine, carbon-rich material is a major component of particulate air pollution and can have detrimental effects on human health and the environment.
Soot analysis is a crucial tool for understanding the sources, composition, and impacts of this pervasive pollutant.
PubCompare.ai's innovative AI-driven platform offers a streamlined and optimized approach to soot analysis research protocols, enhancing reproducibility and enabling researchers to easily locate relevant protocols from literature, pre-prints, and patents.
The platform's powerful comparison tools can help identify the best protocols and products for specific research needs, taking soot analysis to the next level.
Researchers can utilize a range of advanced analytical techniques and equipment to study soot, including Buckyprep, Buckyprep-M, Buckyprep-D, Printex-U, JEM-2100F electron microscopes, and Biflex III and 2200FS microscopes.
The use of combusted argon carrier gas and techniques like EX-24065JGT and Teflon filtration can further enhance the accuracy and precision of soot analysis.
By leveraging PubCompare.ai's cutting-edge platform, researchers can streamline their soot analysis workflows, improve reproducibility, and stay at the forefront of this critical field of study, ultimately contributing to a better understanding of the sources, composition, and environmental impacts of this ubiquitous pollutant.