Surface Area Concentration Research Articles

How Do the Morphology and Crystal Facet of CeO2 Determine the Catalytic Activity toward NO Removal?

Cerium oxide (CeO2) exhibits application potential for the selective catalytic reduction of nitrogen oxides (NOx) with NH3 (NH3-SCR). The crystal facets and morphology of CeO2 have a vital impact on the catalytic performance of NH3-SCR. However, the precise influence mechanisms on SCR activity remain elusive. In this work, CeO2 is successfully synthesized with three distinct crystal facets and nine diverse morphologies. This investigation involves a comprehensive blend of theoretical analysis and experiments, to gain profound insights into the underlying mechanisms governing the SCR catalytic activity concerning morphology and crystal facets. By closely integrating density functional theory (DFT) calculations, Ab initio thermodynamic analysis, SCR catalytic activity experiments, and X-ray photoelectron spectroscopy experiments, it is discovered that the concentration of surface-active oxygen (O*) plays a pivotal role in determining the catalytic activity of CeO2 in SCR reactions, as opposed to factors like specific surface area or oxygen defect concentration. This experimental-theoretical joint study provides design principles of CeO2 catalysts for NO removal.

Enhanced H2S removal efficiency using CuO/Al2O3 catalyst impregnated with Ca(NO3)2: Influence of calcination temperature and mechanistic insights

A series of Ca(NO3)2-CuxO/Al2O3 catalysts with different calcination temperatures were designed to remove H2S effectively. Experimental and characterization results indicate that the calcination temperature can influence the catalyst's specific surface area, basic sites, and oxygen vacancies (VOs) concentration, thereby promoting its desulfurization performance. The Ca(NO3)2-CuxO/Al2O3 catalyst, calcined at 230 °C, exhibits an optimal surface structure. It demonstrates optimal H2S desulfurization performance at a temperature of 60 °C and relative humidity of 60 %, achieving a removal capacity of 486.67 mg/g. The VOs in CuxO (CuO and Cu2O) induce a unique catalytic of H2O, which generates hydroxy radicals (·OH) to oxidize the H2S anion to S. The introduction of Ca2+ provides basic sites to buffer pH, and NO3- with H+ removes H2S through cyclic oxidation, significantly enhancing the desulfurization performance of the Cu-based catalyst. The CaO-CuxO/Al2O3 catalyst, calcined at 700 °C, suffers from a deteriorated catalyst structure, leading to the rapid formation of desulfurization by-products due to the presence of CaO covering the catalyst surface. This diminishes the effectiveness of Cu, significantly impairing the catalyst's desulfurization performance (150.47 mg/g). Consequently, desulfurization mechanisms of Ca(NO3)2-CuxO/Al2O3 and CaO-CuxO/Al2O3 are proposed, offering effective strategies for the design and optimization of subsequent high-efficiency catalysts.

Reaction kinetics of molybdenum dissolution by hydrogen peroxide in acidic and alkaline solutions using tartaric acid and sodium hydroxide: A semi‐empirical model with rotating disc method

AbstractMolybdenum is an amphoteric metal that dissolves in both acidic and alkaline solutions. This fundamental study explores a sustainable process for the dissolution of molybdenum, focusing on the reaction kinetics in H2O2, H2O2‐NaOH, and H2O2‐C4H6O6 solutions. A rotating disc method was applied with the Levich's equation. Semi‐empirical models with activation energy were developed for the H2O2‐NaOH and H2O2‐C4H6O6 solutions. The study examined the effects of rotating speed, disc surface area, temperature, H2O2, NaOH, and C4H6O6 concentrations, along with rotating speed, disc surface area, and temperature. Hydrogen peroxide significantly impacted molybdenum dissolution rates across all three solutions. The reaction order of hydrogen peroxide concentration in the H2O2 solution was greater than that of the H2O2‐NaOH and H2O2‐C4H6O6 solutions. The complex of molybdenum peroxo was formed in H2O2 and H2O2‐NaOH solutions but decomposed at a temperature ≥50°C. The activation energies were determined to be 49.90, 43.60, and 41.10 kJ/mol for the H2O2, H2O2‐NaOH, and H2O2‐C4H6O6 solutions.

Improved morphological stability of NiFe2O4 via Sr doping for chemical looping hydrogen production

As a new hydrogen production technology, chemical looping hydrogen production has the advantage of internal CO2 separation. NiFe2O4 is considered as promising oxygen carrier material for chemical looping hydrogen production due to the high oxygen carrying capacity and low cost. However, rapid redox performance fading caused by structure degradation and interface instability in successive redox reactions that led fto poor stability, and seriously limited the practical application of NiFe2O4 in chemical looping hydrogen production. Herein, we reported a method for improving the morphological stability of NiFe2O4 through Sr doping. NiFe2O4 samples containing different SrO contents (2 wt%, 5 wt% and 8 wt%) were prepared by a simple mechanical mixing method. A series of characterization analysis results showed that adding an appropriate amount of Sr could significantly improve the microstructure, increase the oxygen vacancy concentration and inhibit the phase segregation of NiFe2O4. The NiFe-5Sr presented the largest BET surface area (24.63 m2/g) and highest concentration of oxygen vacancies (77.49 %). NiFe-0Sr and NiFe-2Sr experienced severe phase segregation during continuous cyclic reactions, resulting in significant surface sintering and deactivation. During 20 redox cycles, NiFe-5Sr presented the highest hydrogen yield (around 10 mmol/g) and stable surface morphology. The research results indicated that the optimal doping amount for SrO was 5 %.

Substrate and potential-driven surface morphology of bifunctional Ni-Fe electrode for efficient alkaline water electrolysis

Nickel-iron (Ni-Fe) alloy electrodes are synthesized using chronoamperometry. The influence of substrate type (copper, stainless steel, and nickel) and deposition potential on the structural, morphological, and electrocatalytic characteristics are systematically investigated. X-ray diffraction (XRD) analysis revealed the formation of a face-centered cubic (FCC) Ni-Fe alloy. Electrodeposition at higher potential (−1.45 V) forms well-defined nanoflakes, whereas electrodeposition at lower potential (−1.00 V) results aggregated Ni-Fe particles. The Ni-Fe alloy electrodes having well-defined nanoflakes demonstrated superior electrocatalytic performance, exhibiting a overpotential of −168 mV vs. RHE for the hydrogen evolution reaction (HER) and 236 mV vs. RHE for the oxygen evolution reaction (OER), at current density of 10 mA/cm2. The enhanced electrocatalytic activity of the nanoflakes based Ni-Fe alloy is attributed due to their larger catalytic surface area, porous morphology and higher Fe concentration. The Ni-Fe alloy electrodes displayed bifunctional electrocatalytic behavior, making them highly suitable for both HER and OER processes.

Machine learning-based prediction of adsorption capacity of metal-doped and undoped activated carbon: Assessing the role of metal doping

This research developed five ensemble-based machine learning (ML) models to predict the adsorption capacity of both pristine and metal-doped activated carbon (AC) and identified key influencing features. Results indicated that Extreme Gradient Boosting (XGB) model provided the most accurate predictions for both types of AC, with metal-doped AC exhibiting 1.7 times higher adsorption capacity than pristine AC showing 254.66 and 148.28 mg/g, respectively. Feature analysis using SHAP values revealed that adsorbent characteristics accounted for 53.5 % of the adsorption capacity in pristine AC, while experimental conditions were crucial for metal-doped AC (61.3%), with surface area and initial concentration being the most significant features, showing mean SHAP values of 0.317 and 0.117, respectively. Statistical comparisons of adsorbent characteristics between pristine and metal-doped AC showed that metal doping significantly altered surface area (p-value = 0.0014), pore volume (p-value = 0.0029), and elemental composition (C% (p-value = 3.9513∗10^−7) and O% (p-value = 0.0007)) of AC. Despite the reduction in surface area and consistent pore volume after metal doping, the enhanced adsorption capacity of metal-doped AC was attributed to increased oxygen content from 10.89% to 17.28 % as mean values. This suggests that oxygen-containing functional groups play a critical role in the improved adsorption capacity of metal-doped AC. This research lays the groundwork for optimizing AC adsorbents by identifying key factors in metal-doped AC and suggest further studies on the interaction between specific metal dopants and resulting functional groups to improve adsorption capacity and reduce repeated labor work.

Enhanced phosphorus adsorption using modified drinking water treatment residues: A comparative analysis of powder and alginate bead forms

This study provides an analysis of the phosphorus adsorption efficacy of three modified drinking water treatment residues (MDWTRs): MDWTR-P (powdered form), MDWTR-D2, and MDWTR-D5 (alginate bead-entrapped forms with bead diameters of 2 mm and 5 mm, respectively). The preparation process involved washing and drying the drinking water treatment residue, followed by grinding and sieving to achieve particle sizes below 90 μm. The residue was then incinerated at 600 °C in oxygen-limited conditions. Subsequently, the MDWTR was formulated into alginate beads by mixing with sodium alginate and FeCl3 solutions, resulting in spherical particles of specified diameters. The evaluation of surface area, pore volume, pore size, and CHN concentration revealed that MDWTR-D5 possesses the largest surface area (284.7 m2 g−1) and highest micropore volume (0.04 cm3 g−1), indicating a greater capacity for adsorption. SEM-EDS analysis demonstrated significant compositional changes post-treatment, particularly elevated phosphorus levels, confirming effective adsorption. Metal content analysis indicated high aluminum levels in MDWTR-P and increased iron content in MDWTR-D5. Toxicity Characteristic Leaching Procedure (TCLP) and in vitro bioaccessibility (IVBA) tests confirmed the non-hazardous nature of all MDWTRs, ensuring their safety for environmental applications. Kinetic analyses using pseudo-first-order, pseudo-second-order, and intraparticle diffusion models highlighted the superior performance of MDWTR-D5, with the highest equilibrium adsorption capacity and initial adsorption rate across all tested concentrations, suggesting both high efficiency and rapid adsorption potential. Further validation using Langmuir and Freundlich isotherms revealed MDWTR-D5's highest monolayer adsorption capacity (22.88 mg g−1) and Freundlich adsorption capacity parameter (6.97 mg g−1). Statistical analysis via one-way ANOVA confirmed significant differences in phosphorus concentrations among the MDWTRs samples (p-value <0.001), consistently underscoring MDWTR-D5's superior adsorption performance. These findings highlight MDWTR-D5's potential as an effective adsorbent for phosphorus removal in wastewater treatment, emphasizing its applicability in environmental remediation strategies.

UV-enhanced Mg:MOF-ZnO sensor for n-butanol gas detection at a lower operating temperature

In this study, a novel UV-enhanced Mg-doped MOF-ZnO sensor was investigated for its efficient detection of n-butanol gas at lower temperatures. The composite samples were prepared by the co-precipitation method with the appropriate component ratio. The structure, morphology, and optical properties of the as-prepared samples were systematically analyzed through XRD, SEM, TEM, BET, XPS, and UV–vis characterizations. The gas-sensitive performances based on the pristine MOF-ZnO and Mg-doped MOF-ZnO sensor series with the doping levels of 1 %, 3 %, 5 %, and 7 % (wt%) were comprehensively evaluated by adopting the gas-sensitive system. Among them, the 3 % Mg:MOF-ZnO sensor has given prominence to the largest specific surface area and highest oxygen vacancy concentration demonstrating the most excellent gas-sensing performance. Furthermore, with the aid of UV excitation, the 3 % Mg:MOF-ZnO sensor had achieved the enhanced response value of 2049 that was 4.44 times higher than that of pristine MOF-ZnO to 100 of ppm n-butanol at a low temperature of 246 °C, additionally presented out the superior reproducibility with speedy response/recovery time (12/22 s), the theoretical detection limit as low as 200 ppb, as well as the excellent long-term stability and selectivity for n-butanol. This study offers a promising strategy for the well-development of high-performance n-butanol sensors.

Black carbon and particle lung-deposited surface area in residential wood combustion emissions: Effects of an electrostatic precipitator and photochemical aging

Residential wood combustion (RWC) remains a significant global source of particulate matter (PM) emissions with adverse impacts on regional air quality, climate, and human health. The lung-deposited surface area (LDSA) and equivalent black carbon (eBC) concentrations have emerged as important metrics to assess particulate pollution. In this study we estimated combustion phase-dependent emission factors of LDSA for alveolar, tracheobronchial, and head-airway regions of human lungs and explored the relationships between eBC and LDSA in fresh and photochemically aged RWC emissions. Photochemical aging was simulated in an oxidative flow reactor at OH• exposures equivalent to 1.4 or 3.4 days in the atmosphere. Further, the efficiency of a small-scale electrostatic precipitator (ESP) for reducing LDSA and eBC from the wood stove was determined. For fresh emission eBC correlated extremely well with LDSA, but the correlation decreased after aging. Soot-dominated flaming phase showed the highest eBC dependency of LDSA whereas for ignition and char burning phases non-BC particles contributed strongly the LDSA. Deposition to the alveolar region contributed around 60 % of the total lung-deposition. The ESP was found as an effective method to mitigate particulate mass, LDSA, as well as eBC emissions from wood stoves, as they were reduced on average by 72%, 71%, and 69%, respectively. The reduction efficiencies, however, consistently dropped over the span of an experiment, especially for eBC. Further, the ESP was found to increase the sub-30 nm ultrafine particle number emissions, with implications for LDSA. The results of this study can be used for assessing the contribution of RWC to LDSA concentrations in ambient air.

Promoting low carbon construction using alkali-activated materials: A modeling study for strength prediction and feature interaction

Abstract In place of Portland cement concrete, alkali-activated materials (AAMs) are becoming more popular because of their widespread use and low environmental effects. Unfortunately, reliable property predictions have been impeded by the restrictions of conventional materials science methods and the large compositional variability of AAMs. A support vector machine (SVM), a bagging regressor (BR), and a random forest regressor (RFR) were among the machine learning models developed in this study to assess the compressive strength (CS) of AAMs in an effort to gain an answer to this topic. Improving predictions in this crucial area was the goal of this study, which used a large dataset with 381 points and eight input factors. Also, the relevance of contributing components was assessed using a shapley additive explanations (SHAP) approach. In terms of predicting AAMs CS, RFR outperformed BR and SVM. Compared to the RFR model’s 0.96 R 2, the SVM and BR models’ R 2-values were 0.89 and 0.93, respectively. In addition, the RFR model’s greater accuracy was indicated by an average absolute error value of 4.08 MPa compared to the SVM’s 6.80 MPa and the BR’s 5.83 MPa, which provided further proof of their validity. According to the outcomes of the SHAP research, the two factors that contributed the most beneficially to the strength were aggregate volumetric ratio and reactivity. The factors that contributed the most negatively were specific surface area, silicate modulus, and sodium hydroxide concentration. Using the produced models to find the CS of AAMs for various input parameter values can help cut down on costly and time-consuming laboratory testing. In order to find the best amounts of raw materials for AAMs, academics and industries could find this SHAP study useful.

The effect of doping sulphhydryl-functionalised silica with different diameters on the electro-optical properties of PDLC

ABSTRACT In this study, we synthesised two kinds of spherical SiO2-SH particles with uniform and non-uniform morphologies, and investigated their effects on the electro-optical properties of PDLC. Compared with uniform SiO2-SH nanoparticles, we found that the samples doped with non-uniform SiO2-SH regulate the electro-optical properties of PDLC better. The electro-optical properties test results demonstrated that doping a small amount of SiO2-SH with different sizes significantly reduced the driving voltage of PDLC and maintained a high contrast ratio. SEM results indicated that the size of the polymer meshes was broadened with increasing SiO2-SH doping. This phenomenon can be attributed to the larger specific surface area and higher concentration of sulphhydryl groups in SiO2-SH particles of different sizes. These characteristics enable SiO2-SH to better react with acrylate monomers, thereby improving modulation effects on the electro-optical properties of PDLC. However, when the doping amount exceeded a certain value, the driving voltage of PDLC began to rise, and its electro-optical properties deteriorated because of the agglomeration of nanoparticles at higher doping levels. This study demonstrated that doping SiO2-SH particles of different sizes can optimise the properties of PDLC, which provided new ideas and directions for doping PDLC with nanomaterials.

The Influence of Organic Growing of Maize Hybrids on the Formation of Leaf Surface Area and Chlorophyl Concentration

The Influence of Organic Growing of Maize Hybrids on the Formation of Leaf Surface Area and Chlorophyl Concentration

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites

This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William’s plot, demonstrating a minimal number of outliers (66–71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.

Characterization of size-resolved aerosol hygroscopicity and liquid water content in Nanjing of the Yangtze River Delta

Aerosol hygroscopicity and liquid water content (ALWC) have important influences on the environmental and climate effect of aerosols. In this study, we measured the hygroscopic growth factors (GF) of particles with dry diameters of 40, 80, 150, and 200 nm during the wintertime in Nanjing. Both the GF-derived hygroscopicity parameter (κgf) and ALWC increased with particle size, but displayed differing diurnal variations, with κgf peaking around the midday, while ALWC peaking in the early morning. Nitrate, ammonium and oxygenated organic aerosols (OOA) were found as the chemical components mostly strongly correlated with ALWC. A closure study suggests that during midday photo-oxidation and nighttime high ALWC periods, the κ of organic aerosols (κorg) was underestimated when using previous parameterizations. Accordingly, we re-constructed parameterizations for κorg and the oxidation level of organics for these periods, which indicates a higher hygroscopicity of photochemically formed OOA than the aqueous OOA, yet both being much higher than the generally assumed OOA hygroscopicity. Additionally, in a typical high ALWC episode, concurrently increased ALWC, nitrate, OOA as well as aerosol surface area and mass concentrations were observed under elevated ambient RH. This strongly indicates a coupled effect that the hygroscopic secondary aerosols, in particular nitrate with strong hygroscopicity, led to large increase in ALWC, which in turn synergistically boosted nitrate and OOA formation by heterogeneous/aqueous reactions. Such interaction may represent an important mechanism contributing to enhanced formation of secondary aerosols and rapid growth of fine particulate matter under relatively high RH conditions.

High sensitivity n-butanol gas sensor based on Ho-doped SnO2 nanoparticles

In this paper, series of Ho-doped SnO2 gas sensors were utilized for n-butanol gas detection. The results displayed that minor Ho doping concentration could remarkably improve the sensitivity of SnO2 to n-butanol gas, and the response to 50 ppm of n-butanol gas of 1.0 Ho/SnO2 based sensor reached 160 at 160 °C, which was 4.11 times to that of SnO2 sensor. The enhanced n-butanol gas sensing performance was ascribed to the reduced particle size, the increased BET surface area and oxygen vacancies concentration.

Persulfate Activation Using Biochar from Pomegranate Peel for the Degradation of Antihypertensive Losartan in Water: The Effects of Pyrolysis Temperature, Operational Parameters, and a Continuous Flow Reactor

Biochar derived from pomegranate peel at different pyrolysis temperatures (450, 600, and 850 °C) was synthesized and characterized by BET, XRD, FTIR, and SEM-EDX. Its catalytic efficiency in the degradation of the antihypertensive losartan (LOS) in the presence of sodium persulfate was examined. The biochar pyrolyzed at 850 °C exhibited higher catalytic activity, which was correlated with the greater surface area and higher concentration of minerals on its surface. Interestingly, despite adsorption being favored at alkaline pH, pH 3 showed the highest LOS degradation. LOS decomposition followed pseudo-first-order kinetics. The addition of persulfate significantly increased LOS reduction, while the presence of inorganic and organic water matrix constituents such as sodium chloride, bicarbonate, and humic acid inhibited the oxidation. Experiments conducted with radical scavengers revealed that both hydroxyl and sulfate radicals, as well as singlet oxygen, participated in LOS decomposition, with the former being the dominant species. Using a continuous flow reactor, the system exhibited a satisfactory steady-state performance of 90% LOS removal for 114 h. Afterward, a moderate decrease in performance was observed, which can be attributed to the alteration of the catalyst’s surface and mineral dissolution due to acidity.

Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine

Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm2/cm3 and reached maximum peaks of 2140 μm2/cm3 during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm2/cm3 and 1840 μm2/cm3, respectively. At the same time, average PM1 was about 300 μg/ m3 during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m3. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects.

Ultrafine industrial aerosol as a risk factor for the health of smelting shop workers at a machine-building enterprise

Aim. To evaluate the amount and physicochemical properties of ultrafine industrial aerosol (UIA) in the work zone air (WZA) of smelting shop workers at a machine-building enterprise during various technological processes (melting, welding, and metal machining). Materials and methods. The physical properties of UIA particles were evaluated with NanoScan 3910 scanning spectrometer: the number of particles (number/cm3), particle surface volume (nm3/cm3), particle surface area (nm2/cm2), and nanoparticle mass concentration (μg/cm3) in the WZA of a furnace operator (n = 416), a welder (n = 315), a cutter (n = 286), a grinder (n = 78), and workers of the control group (n = 315). The chemical composition of the air samples was determined by inductively coupled plasma optical emission spectrometry (ICP-OES) using an Optima 2100 DV device. Results. It has been found that the highest concentration of UIA nanoparticles was recorded during metal melting at the workplaces (WP) of furnace operators (4.28 × 104 to 2.41 × 105 particles/cm3) and welders (3.01 × 104 to 3.34 × 105 particles/cm3). During mechanical metal processing, a much smaller number of nanoparticles was produced (for grinders, the number varied from 9.81 × 104 to 1.44 × 105 particles/cm3; for cutters, it varied from 2.71 × 104 to 1.94 × 105 particles/cm3). Indicators of surface area, surface volume and mass concentration at the WPs of furnace operators, welders, grinders and cutters exceeded the corresponding indicators of the control group for almost all sizes of suspended particles with statistically significant differences (p ≤ 0.05). It has been estimated that such metals as Al, Cu, Mg, Mo, Fe, and Ni were present in the workers’ WZA but their content did not exceed the current maximum permissible concentrations. Conclusions. The presence of suspended particles of UIA with a maximum concentration in the range from 20 nm to 70 nm has been confirmed in the WZA during melting, welding of metals and machining. Indicators of concentration, surface area, surface volume, and mass concentration at the workplaces of melting shop workers at the machine-building enterprise significantly exceeded the corresponding indicators in the control group without dust formation processes, showing statistically significant differences (p ≤ 0.05). The evidence of metals in the WZA has suggested their presence in the form of nanoparticles, which are more active and dangerous, thus increasing the risk of their adverse effects on the workers. Ультрадисперсний

Optimising the acid-base ratio of Mg-Al layered double oxides to enhance CO2 capture performance: the critical role of calcination conditions.

The effect of calcination conditions (ramp rate, calcination temperature and time) on the formation of Mg2Al layered double oxides (Mg2Al LDOs) as well as their CO2 capture performance, has been systematically investigated. This study explores novel insights into the intricate relationship between these calcination conditions and the resulting surface characteristics, which play a vital role in CO2 capture efficiency. Notably, it is revealed that a rapid ramp rate (100 °C min-1) significantly increases surface area and hydroxyl concentration, leading to a 69% increase in CO2 capture efficiency compared to slower ramp rate. Conversely, short calcination times (1 h) and fast ramp rates (100 °C min-1) are observed to compromise CO2 adsorption due to the presence of dehydrated LDHs. A critical acid : base ratio of 0.37, achieved from a fast ramp rate (100 °C min-1) at 400 °C for 2 h, was found as a key threshold for optimising surface properties, effectively balancing favourable hydroxyl and less favourable strong acid sites, thereby maximizing CO2 capture performance.

A New Physical Mechanism of Rainfall Facilitation to New Particle Formation

AbstractNewly formed particles and their growth contribute significantly to cloud condensation nuclei. However, the effect of rain on new particle formation (NPF) is poorly understood. Rainfall removes the pre‐existing large particles by below‐cloud scavenging to reduce condensation and coagulation sinks by 4.0 × 10−2 and 1.5 × 10−4 s−1. The scavenging effect largely depends on the amount and duration of rain and the surface area concentration of the raindrops. NPF events can be facilitated by a combination of fewer condensation sinks and favorable meteorological conditions, contributing 3%–47% of PM2.5 (particulate matter smaller than 2.5 μm). The physical mechanism was confirmed by theoretical analysis. The results help elucidate the NPF process from a physical perspective, which can improve the prediction of the occurrence and duration of haze events. Until precursors are reduced, significant reductions in particulate matter (PM) may be difficult because NPF and its growth recharge the atmosphere with PM.