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Volatilization

Volatilization is the process by which a substance is converted from a liquid or solid state into a gaseous state.
This can be an important consideration in various research fields, such as chemistry, environmental science, and materials science, where the behavior of volatile compounds needs to be understood and controlled.
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Most cited protocols related to «Volatilization»

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Publication 2015
Amber Biopharmaceuticals Blood Vessel Buffers Cardiac Arrest Edetic Acid Erythrocytes High-Performance Liquid Chromatographies Hydrogen Sulfide Hypoxia Ions Metals Mus Nitrogen Pentetic Acid Photolysis Plasma Sulfhydryl Compounds Sulfides Tromethamine Volatilization
The non-carcinogenic health risks represented as hazard quotients (HQs) are equal to the chronic daily intake (CDI) divided by the chronic reference dose (RfD). Therefore, the risk assessment models vary depending on exposure routes such as oral ingestion, dermal contact and inhalation.
For ingestion of soil particles, sediment particles, surface water and ground water, the health risk models can be written as Eq. (1)7 22 :

where i represents a target media or land use. IRi represents the ingestion rate, including soil ingestion rate IRs (mg/d), sediment ingestion rate IRsd (mg/d), daily water ingestion rate IRw (mL/d); and EFi represents the exposure frequency (d/yr) for indoor activities EFia, outdoor activities EFoa and swimming EFsd, respectively. ED indicates the exposure duration (yr), BW indicates the average body weight (kg), and AT indicates the average total time (d). RfDingestion denotes the chronic oral reference dose (mg/kg/d). UF represents a unit transfer factor, which is ×10−6 for soil and sediment and ×10−3 for water. Ci indicates the metal concentrations in the target media or land use (mg/kg for soil and sediment and μg/L for water).
The metal concentrations in groundwater Cgw (μg/L) were calculated using a leaching equation and a dilution factor (DF) (Eq. 2)7 :

where Cts (mg/kg) indicates the metal concentrations in the total soil profile (0–100 cm), Kd represents the soil-water partition coefficient (L/kg), θw represents the water-filled soil porosity (Lwater/Lsoil) and ρb is the dry soil bulk density (g/cm3).
The risks arising from intake of heavy metals from vegetables planted in soil can be written as Eq. (3)14 (link):

where Cas (mg/kg) indicates the metal concentrations in the agricultural soils (0–20 cm), PUF (unit-less) represents the plant uptake factor, IRv indicates the vegetable ingestion rate (kg/d), and θv (unit-less) is the vegetable water content.
For dermal contact with soil, sediment and water, the health risk model is written as Eqs (4) and (5)7 22 :
For soil and sediment

For water

where ABS (unit-less) represents the dermal absorption factor for soil and sediment. Kp (cm/h) represents the dermal permeability constant for heavy metals in water. SAi (m2) represents the skin surface area available for exposure in outdoor activities or in swimming. AFi (mg/cm2) represents the soil-to-skin adherence factor for farmers and adults. RfDdermal (mg/kg/d) is the chronic reference dose through dermal contact and UF represents a unit transfer factor (×10−6).
For inhalation routes, the health risk model for risks posed by inhalation of soil particles and vapors is Eq. (6)7 22 .

where, IRa (m3/d) is the air inhalation rate, RfDinhalation (mg/kg/d) is the chronic inhalation reference dose, PEF (m3/kg) indicates the particulate emission factor and VF is the volatilization factor, which in this study was relevant only for elemental Hg (32,376.4 m3/kg)10 (link).
The total hazard quotient (THQ) is the sum of individual HQs for each media or land use, i, and is given by Eq. (7):

in which is calculated only for heavy metals in surface soils. Values of HQ and THQ greater than 1 indicate potential health risks while those less than 1 suggest acceptable risks.
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Publication 2016
Adult Body Weight Carcinogens Dietary Fiber Farmers Health Risk Assessment Inhalation Mercury Metals Metals, Heavy Permeability Plants Respiratory Rate Skin Absorption Technique, Dilution Transfer Factor Vegetables Volatilization
To mimic grazing systems, daily grass intake and excretion of sheep and cattle in the field were quantified based on literature and experimental results from our site. Detailed information that is used to estimate inputs to various N pools in the model is given in Tables 2, 3, 4. As the model does not simulate volatilization, N input from excretion was reduced by a proportion equivalent to 0.6 of the total N in the farm‐yard manure (FYM) without considering the effect of temperature on the volatilization process. It is difficult to model individual animals in the field so we assumed a live weight of 600 kg per animal for beef cattle, 75 kg per animal for ewes and, for fields that were grazed, a spatially uniform distribution of grass intake over the grazing period.
Soil physical and chemical properties of the selected fields were based on baseline field surveys conducted in 2012. Agronomic management quantified for the simulation was interpreted from the farm records for the NWFP. The concentrations of nutrients in applied farmyard manure were estimated based on the DEFRA fertilizer manual (Department for Environment, Food and Rural Affairs, 2010). We assumed that the reported available N content of FYM in the manual is incorporated fully into the soil without further loss.
The SPACSYS model has been parameterized previously for the processes of soil water, soil heat transformation, and C and N cycling (Wu & Shepherd, 2011). Parameters related to grass species were adopted from a previous study. Those parameters were used directly in the simulations.
The data extracted from the UK Climate Projection 2009 (UKCP09) for future climate projections were applied to this study. The UKCP09 weather generator provides probabilistic projections of climate change (Jones et al., 2009). Medium (SRES A1B) and large (SRES A1F1) emission scenarios based on future projections of greenhouse gas and aerosol levels according to the IPCC (IPCC, 2007) were used to generate future climate conditions. The scenarios at the time slices of the 2020s, 2050s and 2080s were considered. One hundred files of 30‐year daily weather variables for each time‐slice under each emission scenario and a baseline representing the 1961–1990 period were generated for the site. To avoid the need for hundreds of simulations with SPACSYS, the mean daily value across the hundred files of each weather element (except precipitation) for each day of the 30 years of data was calculated. Because of its skewed distribution, daily means of precipitation across the files cannot be taken. Therefore, the monthly mean precipitation and the number of rain days per month were calculated for each file, and then both of these elements were averaged across the 100 files. The daily precipitation for a given month was then distributed randomly across the month. As wind speed is not included in UKCP09, it was obtained from the 11‐member Regional Climate Model dataset (Met Office, 2003). Six UKCP09 projections were produced, three time‐slices for medium (represented as 2020med, 2050med and 2080med, respectively) and three time‐slices for large emissions (represented 2020lar, 2050lar and 2080lar, respectively), plus historic climate data over the period 1961–1990 (symbolized as baseline) for the site. Annual mean climate characteristics for the time‐slices under the emission scenarios are given in Table 5.
To avoid further complexity in future scenarios, the current atmospheric CO2 concentration (695 mg CO2 m−3) was applied to all the simulations. Meanwhile, the current farm management practices for the individual fields (e.g. timing and amount of fertilizer or slurry application, grass‐cutting dates, start and end dates of grazing and number of animals) were kept the same for all simulations in the field. Therefore, any change in the fluxes of water, N and C as a result of the treatments would be the consequence of climate change scenarios.
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Publication 2015
Animal Model Animals Beef Cattle chemical properties Climate Climate Change Domestic Sheep Food Greenhouse Gases Nutrients Physical Examination Poaceae Rain Volatilization Wind
The sequential spot sampler consists of a water condensation growth tube followed by a single-jet impactor collector, and is designed to be operated at a flow rate between 1.0 to 1.5 L/min. The growth tube uses the three-stage, moderated laminar flow condensation approach described by Hering et al. (2014) (link). This provides for sufficient supersaturation to activate particles in the nanometer size range, but moderates the flow temperatures and exiting dew point. Particles as small as 8 nm grow through water condensation to form 1–3 μm droplets, and are readily collected as a concentrated spot. The moderator stage reduces the dew point of the flow exiting the growth tube to less than 18°C, thus avoiding vapor condensation on room temperature surfaces. The active well is heated slightly to evaporate water from the droplets during collection, forming a dry spot.
The system is shown in Figure 1. The first two stages of the growth tube, referred to as the “conditioner” and “initiator”, are 154 mm and 73 mm long, and are lined with a 4.8 mm ID wick. The wick is formed by rolling 0.45 μm pore size nylon filter media (Whatman 10416194). A thermoelectric device (TED) mounted between the two stages acts as a heat pump, cooling the conditioner and warming the initiator. A set of fans on the initiator stage dissipate the heat generated by the TED. This upper wick is mounted on a standpipe and wetted by means of a small interior water reservoir at the bottom of the initiator. The third stage, called the “moderator” has a separate wick, 100 mm long and 4.8 mm ID, made from the same nylon filter material, held by a standpipe at the bottom. The moderator stage is cooled by means of a second TED. A parasitic flow of 0.05 L/min is extracted from the system at the base of the moderator to remove water condensate that accumulates around the standpipe.
The flow exits the growth tube through a single, 1.2 mm diameter nozzle and impinges into one of the wells of the multiwell collection plate. The multiwell plate contains 24 evenly spaced wells, each measuring 6 mm diameter, 3 mm depth, and capable of holding 100 μL of solution. A spring loaded mount pushes the well-plate up against a donut-shaped Teflon gasket mounted on the ceiling of the collection chamber that covers all but the active well. This shields the nonactive wells from the flow, and minimizes the head space for volatilization of already-deposited samples. The well-plate has a small, spring-loaded heater that touches the bottom side of the multiwell plate immediately underneath the active well. Its temperature is selected to prevent condensation, and to evaporate droplets as they collect, and its setting depends on the details of the multiwell plate material and thickness. There is also a small heater on the nozzle mount to prevent water condensation in the nozzle. A small stepper motor advances the plate to the next well at the end of each collection period.
A microprocessor controls five temperatures, the conditioner temperature, the initiator-conditioner temperature difference, and the moderator, the impaction nozzle and the multiwell plate temperatures. Under normal operation the conditioner is operated at 2°C– 5°C, the initiator is set to 30°C warmer than the conditioner (ie 32°–35°C), and the moderator is set to 10– 12°C. Maintaining a constant temperature difference between the initiator and conditioner provides the same supersaturation, and hence the same particle activation and growth, regardless of slight variations in the absolute temperatures (see Lewis and Hering, 2013 (link)). The temperature controllers for the conditioner and moderator regulate the power to their respective TEDs, while the controller for the initiator cycles the initiator heat sink fans.
The microprocessor also controls the well-plate position, and logs the instrument status. The user may select the duration for each sample (in minutes or hours) and further has the option for either “synchronized” or “unsynchronized” modes. When “synchronized” the sampling schedule aligns with midnight (e.g., with hourly samples starting at the top of the hour, twice daily samples starting at noon and midnight), while “unsynchronized” sampling starts immediately upon receiving the command. The sample and parasitic water transport flows are controlled by means of a small valve and an external pump. For ambient air sampling an external cyclone, with transport flow as needed, is used to provide an inlet precut, typically 2.5 μm.
Publication 2014
ARID1A protein, human Cyclonic Storms Head Impacted Tooth Medical Devices Nylons SERPINA3 protein, human Teflon Touch Volatilization
The sulfidation process was monitored by measuring time-resolved depletion of soluble sulfide using a sulfide ion selective electrode (sulfide-ISE) following nanoparticle removal by centrifugal ultrafiltration. In a typical experiment, 4.9 mL DI water was added into a 15 mL plastic tube containing desired amount of AgNP-30 nm powder (0.324 – 5.393 mg), followed with sonication in a bath sonicator for 10 min to disperse aggregates, then 0.1 mL of 50 mM Na2S solution was added to initiate the sulfidation reaction at a starting Na2S concentration of 1 mM. The 1 mM Na2S solution in DI water has an initial pH of 11.1 (Orion 8165BNWP pH electrode, Thermo Scientific), and the predominant sulfide species is HS (>99.9%) as calculated by visual MINTEQ (version 3.0). The reaction mixture was rotated at 20 rpm for up to 48 hrs, after which the sulfide-containing solution was separated from the solids using centrifugal ultrafiltration (Amicon Ultra-4 3K, cellulose membrane with 1–2 nm pore size, Millipore), at an relative centrifugal force of 4000 g for 25 min (Allegra X-15R, Beckman Coulter, Inc.). Then, a sulfide antioxidant buffer (SAOB, Orion 941609 Thermo Scientific) was added in equal volume to the collected sulfide-containing filtrate to prevent sulfide oxidation and volatilization during analysis. Soluble sulfide concentrations were measured with a sulfide-ISE (9616BNWP silver/sulfide combination electrode, Thermo Scientific) at room temperature, based on linear calibration curves constructed daily from fresh Na2S standards in SAOB with detection limit of 6.25×10−3 mM. Any interference of silver ion with the sulfide-ISE was prevented by solution pretreatment with EDTA as the manufacturer recommends. This was confirmed by a special control experiment in which sulfide was measured in the presence of excess AgNO3 (Figure S2).
The sulfidation stoichiometry was determined by measuring soluble sulfide depletion over 5-hr for a range of AgNP-30 nm concentrations (0.2 – 10 mM on Ag-atom basis, equivalent to 21.6 – 1079mg/L) and selected sulfidation experiments were carried out under Ar purge to investigate the role of oxygen. Two silver samples with different particle size of 5 nm and 1~3 μm were also used to study surface area dependence. Selected experiments were conducted in media including humic acids (Suwannee River humic acid II standards, International Humic Substances Society) of 20 mg/L, at lower sulfide (0.1 and 0.25 mM), and at lower pH (50 mM pH7 phosphate buffer). All sulfidation experiments were conducted at room temperature and protected from room light.
The role of oxygen was investigated by carrying out sulfidation experiments under Ar purge (99.9% purity) using macroscopic silver foils (4 mm×4 mm×0.127 mm) followed by solid product characterization. In addition, dissolved oxygen (DO) levels were monitored in-situ during batch silver sulfidation experiments in a closed amber glass bottle under magnetic stirring using a DO probe (Orion 083010MD, Thermo Scientific) at 60 sec sampling frequency.
Publication 2011
Allegra Amber Antioxidants Bath Buffers Cellulose Edetic Acid G Force Humic Acids Humic Substances Ion-Selective Electrodes Light Oxygen Phosphates Powder Rivers Silver silver sulfide sodium sulfide Sulfides Tissue, Membrane Ultrafiltration Volatilization

Most recents protocols related to «Volatilization»

The ammonia volatilization was monitored after fertilizer application for up to 10 days using a forced air draft system method (Bhaskar et al., 2022 (link); Stumpe, Vlek & Lindsay, 1984 (link); Bremner, 1965 ). The closed chambers measuring 20 cm × 20 cm × 50 cm size made of 6 mm acrylic sheets were placed in the field. The volatilized NH3 gas from the soil surface under different treatments was collected in a 2% Boric acid solution containing a mixed indicator (methyl red and bromocresol green). The air inside the chamber was collected into boric acid traps using a vacuum pump having a flow rate of 3 L min−1. The boric acid traps were changed every 24 h. The volatilized NH3 was determined by the titration of boric acid solution with 0.02 N sulphuric acid, and further calculations were done using the formula given below (Eq. (1)).
Volatilizedammonia(mgpermsq.perday)=A0.000281000LB
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Publication 2023
Ammonia boric acid Bromcresol Green Sulfuric Acids Titrimetry Vacuum Volatilization
Fe3O4–OA were prepared by the chemical co-precipitation. In a typical procedure, 30 mL of 5.1% FeCl2 solution was slowly dropped into the 20 mL of 5.5% FeCl3 solution under mechanical stirring and nitrogen atmosphere. After adding 50 mL of concentrated ammonia aqueous drop by drop within 30 min, the reaction mixture was heated to 80 °C, and 4 mL of oleic acid was quickly injected into the mixture subsequently. After 3 h, the reaction system was heated to 85 °C and remained for 30 min for complete volatilization of NH3. pH was adjusted to neutral, and the residual ions were removed by rinsing with acetone and ultrapure water for 3–4 times in magnetic separation principle. Finally, hydrophobic Fe3O4–OA NPs were collected as Fe3O4–OA magnetic fluid with a solid content of 5 mg mL−1, via dispersing in an appropriate volume of n-octane and eliminating the acetone via mechanical stirring at 80 °C.
Publication 2023
Acetone Ammonia Atmosphere Chemical Precipitation Ions Nitrogen octane Oleic Acid Oxide, Ferrosoferric Volatilization
Total N output of agricultural systems was calculated by adding N removal from cropland through crop N uptake, gaseous emissions (ammonia (NH3) volatilization, nitrous oxide (N2O) emission, and nitric oxide (NO) emission), and hydrous loss.
We calculated the annual N crop uptake for each crop harvested in each EAC state by multiplying its annual yield reported in the FAOstat database (FAOSTAT 2018 ) by its N content (Lassaletta et al. 2014 ). The annual total N crop uptake was estimated by adding up the annual N crop uptake for all crops harvested. The application of SNF and ANM to cropland is accompanied by N losses through different pathways. We estimated NH3 volatilization, N2O, and NO emissions based on specific emission factors (Table S4) (Bouwman et al. 2002 ; FAO 2001 ). NH3 volatilization emission factors vary with cropland type (upland crops and wetland rice) and N source. To estimate the annual quantity of NH3 gaseous emissions from cropland after fertilizer application, we multiplied regional emission factors reported in Bouwman et al. (2002 ) by annual input of SNF and ANM. To estimate annual N2O and NO emissions from cropland, we based our estimates on emission factors for developing countries (FAO 2001 ), multiplying them with annual input of SNF and ANM. We estimated the annual quantity of N loss through leaching from the applied fertilizer by assuming a 10% loss (3% and 7% of applied ANM and SNF, respectively), taking into consideration the low fertilizer uses and the region's highest rainfall (Table S1). The rate at which added fertilizer is leached from the soil may depend on various factors, including rainfall, type of crop, physical and biochemical properties of the soil, management practices, and more (Boumans et al. 2005 (link); Musyoka et al. 2019 (link); Zheng et al. 2019 ). For this, we based our assumption on the study by Ross et al. (2008 (link)), which revealed that the amount of N loss by leaching from the applied N fertilizer may range from 5% to 50%.
Publication 2023
Ammonia Crop, Avian Gases Oryza sativa Oxide, Nitric Physical Examination Volatilization Wetlands
As shown in Fig. 1, the preparation of the dual shell microcapsules includes three steps. Firstly, a micron-sized emulsion was firstly obtained after stirring the mixture of STF, liquid paraffin, and Span80 (as emulsifier) at a low rotation speed (Fig. 1(a)). Then, the polycondensation occurs between the PEG in the STF droplets and CD-MDI at the surface of the emulsion to form a preliminary polyurethane shell layer (Fig. 1(b)). Finally, the unreacted isocyanate on the surface of polyurethane shell layer reacts with DETA to form a dense polyurea shell layer (Fig. 1(c)). As a result, dual shell microcapsules are formed.
The dispersed particles adopted for the preparation of STF are solid silica microspheres with a particle size of about 150 nm, which plays a decisive role in the shear thickening performance of STF, as shown in Fig. 1(e). To investigate the shear thickening property of the STF, the rheological tests of STF with different silica concentrations are carried out (details in ESI). As shown in Fig. 1(f), the viscosity of SiO2/PEG200 fluids firstly decreases with the increase in the shear rate, then increases rapidly after a critical shear rate is reached. The higher the concentration of silica, the lower the critical shear rate and the faster the viscosity mutation. When the concentration of silica is 68.5%, after a critical shear rate at 60 s−1 was reached, the viscosity increases rapidly and the value at the peak was 28 times larger than the initial value. To make sure that STF can be suspended in the solvent, the STF with lower concentration (62.0%) is chosen. Nevertheless, the consumption of PEG during the following reaction process will increase the concentration of silica, which ensures the good shear thickening performance (details in ESI). This ingenious design not only ensures the dispersion of STF but also maintains good shear thickening performance.
The emulsification effect of STF in liquid paraffin was observed by optical microscopy and the prepared double-layered microcapsules, and the cross-sections of composites were observed by SEM, as shown in Fig. 2(a). It can be seen from Fig. 2(a1) and (a4) that STF emulsification in liquid paraffin is well dispersed. The average droplet diameter is 100 μm with an agitation rate of 800 rpm. As shown in Fig. 2(a2) and (a3), the spherical particle size and double layered microcapsule wall are 190 μm and 14.31 μm, respectively. The surface of the microcapsules has a certain roughness, which is believed to be caused by the uneven shrinkage of wall materials caused by the rapid evaporation of solvent in the drying process and the certain adhesion between microcapsules in the emulsion reaction. We also used drop addition to prepare STF capsules for comparison (details in ESI).
To investigate the structure of the core material, pure wall material, and microcapsules, the FTIR test was carried out, and the results are shown in Fig. 2(b). The peak at 1082 cm−1 corresponds to the asymmetric and symmetric vibrations of the Si–O–Si groups of the silica microspheres in the core material STF, which could also be observed in the spectra of the microcapsules. In the spectra of b2 and b3, the carbonyl peaks in the range of 1646–1543 cm−1 and the peak of the stretching vibration of –NH at 3279 cm−1 are observed. The same absorption peak also appears in the spectra of microcapsules, which confirms the formation of polyurea and polyurethane. By comparing the spectra of b3 and b4, the microcapsules and polyurea have the same characteristic absorption peaks at 2922 cm−1 and 2854 cm−1, which further indicates that the outermost layer of the microcapsules is the polyurea shell. According to the infrared spectrum, the absorption characteristic peaks of the STF and the polyurea-polyurethane shell can be observed, which confirms the successful encapsulation of STF within the microcapsules.
Besides, the thermogravimetric analysis of the double-layered microcapsules, pure core material, and pure wall material are shown in Fig. 2(c). According to Fig. 2(c1), the STF shows only one thermal degradation stage from 150 °C to 370 °C, which corresponds to the thermal decomposition process of the PEG contained in it. The weight of the pure core material (STF) decreases rapidly at 225 °C. In comparison, the microcapsule with STF as the core shows two weight loss stages (Fig. 2(c4)), indicating the successful encapsulation of STF in the PU/PUA shell. Moreover, the initial decomposition temperature of the microcapsule is quite close to that of the STF, which indicates that the weight loss of the first stage at 240 °C mainly arises from the volatilization and decomposition of the STF. By comparing Fig. 2(c2–c4), it clearly shows that the decomposition temperature of the polyurethane shell and polyurea shell is 320 °C, proving that the core material has a good coating effect under PU and PUA shell. Compared to Fig. 2(c1), the thermal weight loss temperature point of STF in microcapsule increases from 225 °C to 240 °C and the weight loss speed of STF slows down. This indicates that the polyurea polyurethane double-layered microcapsules have good thermal protection to the core material. The polyurea polyurethane shell can not only improve the service temperature of STF but also slows down the leakage of STF.
Publication 2023
Capsule DEET Emulsions Light Microscopy Microcapsules Microspheres Mutation Oil, Mineral polyurea polyurethane isocyanate Polyurethanes Silicon Dioxide Solvents Spectroscopy, Fourier Transform Infrared Vibration Viscosity Vision Volatilization
Ammonia volatilization was measured using the open chamber technique, as described by Araújo et al.51 (link). The ammonia chamber was made of a 2-L volume polyethylene terephthalate (PET) bottle. The bottom of the bottle was removed and used as a cap above the top opening to keep the environment controlled, free of insects and other sources of contamination. An iron wire was used to support the plastic jar. A strip of polyfoam (250 mm in length, 25 mm wide, and 3 mm thick) was soaked in 20 ml of acid solution (H2SO4 1 mol dm−3 + glycerine 2% v/v) and fastened to the top, with the bottom end of the foam remaining inside the plastic jar. Inside each chamber there was a 250-mm long wire designed with a hook to support it from the top of the bottle, and wire basket at the bottom end to support a plastic jar (25 mL) that contained the acid solution to keep the foam strip moist during sampling periods (Fig. 7). The ammonia chambers were placed installed in the bucket located in the middle of each experimental block after the last gas sampling of the day and removed before the start of the next gas sampling.

Mobile ammonia chamber details for ammonia measurement in dung beetle trial. Adapted from Araújo et al.51 (link).

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Publication 2023
Acids Ammonia Beetles Feces Glycerin Insecta Iron Polyethylene Terephthalates Volatilization

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More about "Volatilization"

Volatilization is the process of converting a substance from a liquid or solid state into a gaseous state.
This phase transition can be an important consideration in various research fields, including chemistry, environmental science, and materials science, where the behavior of volatile compounds needs to be understood and controlled.
The SpectraMax M5e, DMA-80, and STA 449 F5 are analytical instruments that can be used to study volatilization processes.
These devices allow researchers to measure and analyze the properties of volatile substances, such as their vapor pressure, evaporation rate, and thermal behavior.
The Probe sonicator is another tool that can be used to facilitate volatilization.
This device uses high-frequency sound waves to create cavitation, which can help to vaporize liquids and solubilize compounds.
Acetone is a common solvent used in volatilization studies, as it is highly volatile and can be easily evaporated.
The HS100 and Silicone oil emulsion are also related to volatilization, as they can be used to control the release and evaporation of volatile compounds.
The PTP 6+6 Peltier System and PCB 1500 Water Peltier System are temperature control devices that can be used to regulate the conditions during volatilization experiments.
These systems can help to ensure reproducible and reliable results.
Finally, the TRIzol reagent is a solution used in molecular biology to extract RNA from biological samples.
The volatilization of the reagent components can be an important consideration when using this product.
Overall, understanding and optimizing volatilization is crucial for researchers working in a variety of fields.
PubCompare.ai's AI-driven platform can help to streamline this process by providing access to a vast database of protocols and allowing users to identify the most effective and reproducible methods for their research.