Kinetic Computation of Cyclization Reactions of Large Keto-Hydroperoxide Radicals in Low Temperature Combustion.

The cyclization reactions of keto-hydroperoxide (KHP) radicals leading to the formation of keto cyclic ethers and OH radicals play an important role in low temperature combustion for hydrocarbon fuels or oxygenated hydrocarbon fuels. However, due to the lack of kinetic data of cyclization reactions of KHP radicals, researchers often derive high-pressure-limit rate constants of cyclization reactions of KHP radicals from analogous cyclization reactions of hydroperoxyl alkyl radicals during construction of the combustion mechanism. This study aims to systematically investigate the kinetics of cyclization reactions of KHP radicals involving short-to-large-sized radicals. The studied reactions are divided into 7 reaction classes, according to the size of the cyclic transition state, the conjugative effect (whether KHP radicals are resonance-stabilized or not), and the position of the carbonyl group (whether the carbonyl group is inside or outside of the reaction center). The isodesmic reaction method, in conjunction with transition state theory, is utilized for each reaction class to compute the energy barriers and high-pressure-limit rate constants at the DFT level. The study revealed that energy barriers calculated at the DFT level with correction by the isodesmic reaction method are close to the results from the benchmark CCSD(T) method. To develop more accurate rate rules, these reaction classes are further divided into subclasses based on the relative site of the OOH group with the carbonyl group, the type of carbon atoms where the OOH group is located, and the type of carbon atoms where the radical site is located. For each subclass, high-pressure-limit rate rules are derived by averaging the rate constants of reactions in the subclass, and it is found that the maximum absolute deviation of the energy barrier and the ratio of the largest rate constant to the smallest rate constant among reactions in each subclass are within chemical accuracy limits, indicating acceptable use of the developed rate rules. A comparison of the rate constants for cyclization reactions of KHP radicals with the values of analogous cyclization reactions of hydroperoxyalkyl radicals as provided in reported mechanisms is made. Additionally, a comparison is drawn between our developed rate rules for subclasses of the cyclization reactions of KHP radicals and the rate rules for analogous subclasses of cyclization reactions of hydroperoxyl alkyl radicals. These comparisons demonstrate significant differences and highlight the necessity for improved rate rules for cyclization reactions of KHP radicals to enhance the automatically generated combustion mechanisms for hydrocarbon and oxygenated hydrocarbon fuels.

A Fast Algorithm for Estimating Two-Dimensional Sample Entropy Based on an Upper Confidence Bound and Monte Carlo Sampling.

The two-dimensional sample entropy marks a significant advance in evaluating the regularity and predictability of images in the information domain. Unlike the direct computation of sample entropy, which incurs a time complexity of O(N2) for the series with N length, the Monte Carlo-based algorithm for computing one-dimensional sample entropy (MCSampEn) markedly reduces computational costs by minimizing the dependence on N. This paper extends MCSampEn to two dimensions, referred to as MCSampEn2D. This new approach substantially accelerates the estimation of two-dimensional sample entropy, outperforming the direct method by more than a thousand fold. Despite these advancements, MCSampEn2D encounters challenges with significant errors and slow convergence rates. To counter these issues, we have incorporated an upper confidence bound (UCB) strategy in MCSampEn2D. This strategy involves assigning varied upper confidence bounds in each Monte Carlo experiment iteration to enhance the algorithm's speed and accuracy. Our evaluation of this enhanced approach, dubbed UCBMCSampEn2D, involved the use of medical and natural image data sets. The experiments demonstrate that UCBMCSampEn2D achieves a 40% reduction in computational time compared to MCSampEn2D. Furthermore, the errors with UCBMCSampEn2D are only 30% of those observed in MCSampEn2D, highlighting its improved accuracy and efficiency.

Inhibiting Mg Diffusion and Evaporation by Forming Mg-Rich Reservoir at Grain Boundaries Improves the Thermal Stability of N-Type Mg3 Sb2 Thermoelectrics.

N-type Mg3 Sb2 -based thermoelectric materials show great promise in power generation due to their mechanical robustness, low cost of Mg, and high figure of merit (ZT) over a wide range of temperatures. However, their poor thermal stability hinders their practical applications. Here, MgB2 is introduced to improve the thermal stability of n-type Mg3 Sb2 . Enabled by MgB2 decomposition, extra Mg can be released into the matrix for Mg compensation thermodynamically, and secondary phases of Mg─B compounds can kinetically prevent Mg diffusion along grain boundaries. These synergetic effects inhibit the formation of Mg vacancies at elevated temperatures, thereby enhancing the thermal stability of n-type Mg3 Sb2 . Consequently, the Mg3.05 (Sb0.75 Bi0.25 )1.99 Te0.01 (MgB2 )0.03 sample exhibits negligible variation in thermoelectric performance during the 120-hour continuous measurement at 673 K. Moreover, the ZT of n-type Mg3 Sb2 can be maintained by adding MgB2 , reaching a high average ZT of ≈1.1 within 300-723 K. An eight-pair Mg3 Sb2 -GeTe-based thermoelectric device is also fabricated, achieving an energy conversion efficiency of ≈5.7% at a temperature difference of 438 K with good thermal stability. This work paves a new way to enhance the long-term thermal stability of n-type Mg3 Sb2 -based alloys and other thermoelectrics for practical applications.

Accurate Kinetics of Cyclization Reactions of the Large-Size Hydroperoxy Methyl-Ester Radicals Investigated by the Isodesmic Reaction Correction Method.

The cyclization reactions of hydroperoxymethylester radicals are pivotal in low-temperature methyl-ester combustion but limited experimental and theoretical kinetic data pose challenges. Prior research has drawn upon analogous hydroperoxy alkyl radical cyclization reactions to approximate rate constants and might inaccurately represent ester group-specific behavior. This study systematically investigates these kinetics, accounting for ester group effects and computational complexities in large molecular systems. The reactions are categorized into 11 classes based on cyclic transition state size and -OOH/radical positions. Energy barriers and high-pressure-limit rate constants are calculated using the isodesmic reaction correction method, validated, and applied to 24 subclasses based on carbon sites connected to -OOH and radical moieties. Subclass high-pressure-limit rate rules are derived through averaging rate constants. Analysis reveals uncertainties within acceptable chemical accuracy limits, validating the reaction classification and rate rules. We conduct comparative analyses with values from analogous alkyl reactions in established mechanisms while comparing our results with the high-pressure-limit rate rules for analogous alkane reactions. These comparisons reveal notable disparities, emphasizing the ester group's influence and necessitating tailored ester-specific rate rules. These findings hold promise for improving automatic reaction mechanism generation, particularly for large methyl esters.

Investigating the kinetics of the intramolecular H-migration reaction class of methyl-ester peroxy radicals in low-temperature oxidation mechanisms of biodiesel.

Biodiesel is a promising, sustainable, and carbon-neutral fuel. However, studying its combustion mechanisms comprehensively, both theoretically and experimentally, presents challenges due to the complexity and size of its molecules. One significant obstacle in determining low-temperature oxidation mechanisms for biodiesel is the lack of kinetic parameters for the reaction class of intramolecular H-migration reactions of alkyl-ester peroxy radicals, labeled as R(CO)OR'-OO˙ (where the 'dot' represents the radical). Current biodiesel combustion mechanisms often estimate these parameters from the analogous reaction class of intramolecular H-migration reactions of alkyl peroxy radicals in alkane combustion mechanisms. However, such estimations are imprecise and neglect the unique characteristics of the ester group. This research aims to explore the kinetics of the reaction class of H-migration reactions of methyl-ester peroxy radicals. The reaction class is divided into 20 subclasses based on the newly formed cycle size of the transition state, the positions of the peroxy radical and the transferred H atom, and the types of carbons from which the H atom is transferred. Energy barriers for each subclass are calculated by using the CBS-QB3//M06-2X/6-311++G(d,p) method. High-pressure-limit and pressure-dependent rate constants ranging from 0.01 to 100 atm are determined using the transition state theory and Rice-Ramsberger-Kassel-Marcus/master-equation method, respectively. It is noted that the pressure-dependent rate constants calculated for each individual isomerization channel could bring some uncertainties while neglecting the interconnected pathways. A comprehensive comparison is made between our values of selected reactions and high-level calculated values of the corresponding reactions reported in the literature. The small deviation observed between these values indicates the accuracy and reliability of the energy barriers and rate constants calculated in this study. Additionally, our calculated high-pressure-limit rate constants are compared with the corresponding values in combustion mechanisms of esters, which were estimated based on analogous reactions of alkyl peroxy radicals. These comparative analyses shed light on the significant impact of the ester group on the kinetics, particularly when the ester group is involved in the reaction center. Finally, the high-pressure-limit rate rule and pressure-dependent rate rule for each subclass are derived by averaging the rate constants of reactions in each subclass. The accurate and reasonable rate rules for methyl-ester peroxy radicals developed in this study play a crucial role in enhancing our understanding of the low-temperature oxidation mechanisms of biodiesel.

Pd-Doped Co3 O4 Nanoarray for Efficient Eight-Electron Nitrate Electrocatalytic Reduction to Ammonia Synthesis.

Ammonia (NH3 ) is an indispensable feedstock for fertilizer production and one of the most ideal green hydrogen rich fuel. Electrochemical nitrate (NO3 - ) reduction reaction (NO3 - RR) is being explored as a promising strategy for green to synthesize industrial-scale NH3 , which has nonetheless involved complex multi-reaction process. This work presents a Pd-doped Co3 O4 nanoarray on titanium mesh (Pd-Co3 O4 /TM) electrode for highly efficient and selective electrocatalytic NO3 - RR to NH3 at low onset potential. The well-designed Pd-Co3 O4 /TM delivers a large NH3 yield of 745.6 µmol h-1 cm-2 and an extremely high Faradaic efficiency (FE) of 98.7% at -0.3 V with strong stability. These calculations further indicate that the doping Co3 O4 with Pd improves the adsorption characteristic of Pd-Co3 O4 and optimizes the free energies for intermediates, thereby facilitating the kinetics of the reaction. Furthermore, assembling this catalyst in a Zn-NO3 - battery realizes a power density of 3.9 mW cm-2 and an excellent FE of 98.8% for NH3 .

ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data.

Understanding cellular metabolism is important across biotechnology and biomedical research and has critical implications in a broad range of normal and pathological conditions. Here, we introduce the user-friendly web-based platform ImmCellFie, which allows the comprehensive analysis of metabolic functions inferred from transcriptomic or proteomic data. We explain how to set up a run using publicly available omics data and how to visualize the results. The ImmCellFie algorithm pushes beyond conventional statistical enrichment and incorporates complex biological mechanisms to quantify cell activity. For complete details on the use and execution of this protocol, please refer to Richelle et al. (2021).1.

Tailorable Lignocellulose-Based Aerogel to Achieve the Balance between Evaporation Enthalpy and Water Transport Rate for Efficient Solar Evaporation.

Solar-driven interfacial evaporation technology has become an effective approach to alleviate freshwater shortage. To improve its evaporation efficiency, the pore-size dependence of the water transport rate and evaporation enthalpy in the evaporator should be further investigated. Based on the transportation of water and nutrients in natural wood, we facilely designed a lignocellulose aerogel-based evaporator using carboxymethyl nanocellulose (CMNC) cross-linking, bidirectional freezing, acetylation, and MXene-coating. The pore size of the aerogel was adjusted by controlling its CMNC content. When the channel diameter of the aerogel-based evaporator increased from 21.6 to 91.9 µm, the water transport rate of the proposed evaporator increased from 31.94 to 75.84 g min-1, while its enthalpy increased from 1146.53 to 1791.60 kJ kg-1. At a pore size of 73.4 µm, the evaporation enthalpy and water transport rate of the aerogel-based evaporator achieved a balance, leading to the best solar evaporation rate (2.86 kg m-2 h-1). The evaporator exhibited excellent photothermal conversion efficiency (93.36%) and salt resistance (no salt deposition after three cycles of 8 h). This study could guide the development of efficient solar-driven evaporators for seawater desalination.

Constructing Co@TiO2 Nanoarray Heterostructure with Schottky Contact for Selective Electrocatalytic Nitrate Reduction to Ammonia.

Electrochemical nitrate (NO3 - ) reduction reaction (NO3 - RR) is a potential sustainable route for large-scale ambient ammonia (NH3 ) synthesis and regulating the nitrogen cycle. However, as this reaction involves multi-electron transfer steps, it urgently needs efficient electrocatalysts on promoting NH3  selectivity. Herein, a rational design of Co nanoparticles anchored on TiO2  nanobelt array on titanium plate (Co@TiO2 /TP) is presented as a high-efficiency electrocatalyst for NO3 - RR. Density theory calculations demonstrate that the constructed Schottky heterostructures coupling metallic Co with semiconductor TiO2  develop a built-in electric field, which can accelerate the rate determining step and facilitate NO3 - adsorption, ensuring the selective conversion to NH3 . Expectantly, the Co@TiO2 /TP electrocatalyst attains an excellent Faradaic efficiency of 96.7% and a high NH3  yield of 800.0 µmol h-1  cm-2  under neutral solution. More importantly, Co@TiO2 /TP heterostructure catalyst also presents a remarkable stability in 50-h electrolysis test.

High-efficiency electroreduction of nitrite to ammonia on a Cu@TiO2 nanobelt array.

Electrochemical nitrite (NO2-) reduction is a potential and sustainable route to produce high-value ammonia (NH3), but it requires highly active electrocatalysts. Herein, Cu nanoparticles anchored on a TiO2 nanobelt array on a titanium plate (Cu@TiO2/TP) are reported as a high-efficiency electrocatalyst for NO2--to-NH3 conversion. The designed Cu@TiO2/TP catalyst exhibits outstanding catalytic performance toward the NO2-RR, with a high NH3 yield of 760.5 µmol h-1 cm-2 (237.7 µmol h-1 mgcat.-1) and an excellent faradaic efficiency of 95.3% in neutral solution. Meanwhile, it also presents strong electrochemical stability during cyclic tests and long-term electrolysis.

Ambient ammonia synthesis via nitrite electroreduction over NiS2 nanoparticles-decorated TiO2 nanoribbon array.

Nitrite (NO2-), as a N-containing pollutant, widely exists in aqueous solution, causing a series of environmental and health problems. Electrocatalytic NO2- reduction is a promising and sustainable strategy to remove NO2-, meanwhile, producing high value-added ammonia (NH3). But the NO2- reduction reaction (NO2-RR) involves complex 6-electron transfer process that requires high-efficiency electrocatalysts to accomplish NO2--to-NH3 conversion. Herein, we report NiS2 nanoparticles decorated TiO2 nanoribbon array on titanium mesh (NiS2@TiO2/TM) as a fantastic NO2-RR electrocatalyst for ambient NH3 synthesis. When tested in NO2--containing solution, NiS2@TiO2/TM achieves a satisfactory NH3 yield of 591.9 µmol h-1 cm-2 and a high Faradaic efficiency of 92.1 %. Besides, it shows remarkable stability during 12-h electrolysis test.

Boosting electrochemical nitrate-to-ammonia conversion by self-supported MnCo2O4 nanowire array.

Direct electrocatalytic reduction of nitrate (NO3-) is an efficient route to simultaneously synthesize ammonia (NH3) and remove NO3- pollutants under ambient conditions, however, it is hindered by the lack of efficient and stable catalysts. Herein, a self-supported spinel-type MnCo2O4 nanowire array is demonstrated for exclusively catalyzing the conversion of NO3- to NH3, achieving a high Faradic efficiency of 97.1% and a large NH3 yield of 0.67 mmol h-1 cm-2. Furthermore, density functional analysis reveals that MnCo2O4 (220) surface has high activity for NO3- reduction with a low energy barrier of 0.46 eV for *NO to *NOH.

CoS2@TiO2 nanoarray: a heterostructured electrocatalyst for high-efficiency nitrate reduction to ammonia.

Electrochemical nitrate (NO3-) reduction emerges as a promising strategy to maintain the balance of the global nitrogen cycle and an alternative to nitrogen electroreduction for ambient ammonia (NH3) synthesis. However, the complicated multiple-electron transfer process of NO3--to-NH3 conversion demands catalysts with high selectivity for NH3 production. Herein, CoS2 nanoparticle decorated TiO2 nanobelt array on a titanium plate (CoS2@TiO2/TP) is reported as a superb electrocatalyst for the NO3- reduction reaction. In 0.1 M NaOH containing 0.1 M NO3-, CoS2@TiO2/TP offers a large NH3 yield of 538.21 µmol h-1 cm-2 at -0.7 V vs. reversible hydrogen electrode (RHE) and a high faradaic efficiency of 92.80% at -0.5 V vs. RHE. Additionally, it also shows strong stability for the 20 h electrolysis test.

High-Efficiency Electrochemical Nitrate Reduction to Ammonia on a Co3O4 Nanoarray Catalyst with Cobalt Vacancies.

Electrocatalytic nitrate reduction reaction (NO3RR) affords a bifunctional character in the carbon-free ammonia synthesis and remission of nitrate pollution in water. Here, we fabricated the Co3O4 nanosheet array with cobalt vacancies on carbon cloth (vCo-Co3O4/CC) by in situ etching aluminum-doped Co3O4/CC, which exhibits an excellent Faradaic efficiency of 97.2% and a large NH3 yield as high as 517.5 µmol h-1 cm-2, better than the pristine Co3O4/CC. Theoretical calculative results imply that the cobalt vacancies can tune the local electronic environment around Co sites of Co3O4, increasing the charge and reducing the electron cloud density of Co sites, which is thus conducive to adsorption of NO3- on Co sites for greatly enhanced nitrate reduction. Furthermore, the vCo-Co3O4 (311) facet presents excellent NO3RR activity with a low energy barrier of about 0.63 eV on a potential-determining step, which is much smaller than pristine Co3O4 (1.3 eV).

Ni nanoparticle-decorated biomass carbon for efficient electrocatalytic nitrite reduction to ammonia.

Electrocatalytic nitrite (NO2-) reduction to ammonia (NH3) can not only synthesize value-added NH3, but also remove NO2- pollutants from the environment. However, the low efficiency of NO2--to-NH3 conversion hinders its applications. Here, Ni nanoparticle-decorated juncus-derived biomass carbon prepared at 800 °C (Ni@JBC-800) serves as an efficient catalyst for NH3 synthesis by selective electroreduction of NO2-. This catalyst shows a remarkable NH3 yield of 4117.3 µg h-1 mgcat.-1 and a large faradaic efficiency of 83.4% in an alkaline electrolyte. The catalytic mechanism is further investigated by theoretical calculations.

Cu nanoparticles decorated juncus-derived carbon for efficient electrocatalytic nitrite-to-ammonia conversion.

Electrocatalytic nitrite reduction to value-added NH3 can simultaneously achieve sustainable ammonia production and N-contaminant removal in natural environments, which has attracted widespread attention but still lacks efficient catalysts. In this work, Cu nanoparticles decorated juncus-derived carbon can be proposed as a high-active electrocatalyst for NO2--to-NH3 conversion, obtaining a high Faradaic efficiency of 93.2% and a satisfactory NH3 yield of 523.5 µmol h-1 mgcat.-1. Density functional theory calculations were applied to uncover insightful understanding of internal catalytic mechanism.

High-Pressure-Limit Rate Coefficients for HO2 Elimination Reactions of Hydroperoxyalkenylperoxy Radicals based on the Reaction Class Transition State Theory.

Thermokinetic parameters and transport parameters are of great importance to the combustion model and the reaction rate rules are of great importance to construct the combustion reaction mechanism for hydrocarbon fuels. The HO2 elimination reaction class for hydroperoxyalkenylperoxy radicals is one of the key reaction classes for olefin, for which the rate coefficients are lacking. Therefore, the rate coefficients and rate rules of the HO2 elimination reaction class for hydroperoxyalkenylperoxy radicals are studied in this work. The reaction class transition state theory (RC-TST) is used to calculate the rate coefficients. In addition, the HO2 elimination reaction class of hydroperoxyalkenylperoxy radicals is divided into four subclasses depending upon the type of H-Cß bond that is broken in the reactant molecules, and the rate rules are calculated by taking the average of rate coefficients from a representative set of reactions in a subclass. The calculated kinetics data would be valuable for the construction of the combustion reaction mechanism for olefin.

Machine Learning-Based Prediction Method for Tremors Induced by Tacrolimus in the Treatment of Nephrotic Syndrome.

Tremors have been reported even with a low dose of tacrolimus in patients with nephrotic syndrome and are responsible for hampering the day-to-day work of young active patients with nephrotic syndrome. This study proposes a neural network model based on seven variables to predict the development of tremors following tacrolimus. The sensitivity and specificity of this algorithm are high. A total of 252 patients were included in this study, out of which 39 (15.5%) experienced tremors, 181 patients (including 32 patients who experienced tremors) were randomly assigned to a training dataset, and the remaining were assigned to an external validation set. We used a recursive feature elimination algorithm to train the training dataset, in turn, through 10-fold cross-validation. The classification performance of the classifer was then used as the evaluation criterion for these subsets to find the subset of optimal features. A neural network was used as a classification algorithm to accurately predict tremors using the subset of optimal features. This model was subsequently tested in the validation dataset. The subset of optimal features contained seven variables (creatinine, D-dimer, total protein, calcium ion, platelet distribution width, serum kalium, and fibrinogen), and the highest accuracy obtained was 0.8288. The neural network model based on these seven variables obtained an area under the curve (AUC) value of 0.9726, an accuracy of 0.9345, a sensitivity of 0.9712, and a specificity of 0.7586 in the training set. Meanwhile, the external validation achieved an accuracy of 0.8214, a sensitivity of 0.8378, and a specificity of 0.7000 in the validation dataset. This model was capable of predicting tremors caused by tacrolimus with an excellent degree of accuracy, which can be beneficial in the treatment of nephrotic syndrome patients.

Missense Variant rs28362680 in BTNL2 Reduces Risk of Coronary Heart Disease.

Background: The pathological basis of coronary heart disease (CHD) is atherosclerosis. BTNL2 can inhibit the activation of T cells. We aimed to explore the association between BTNL2 genetic variants and CHD risk in the southern Chinese Han population. Methods: We recruited 1419 participants to perform an association analysis between missense variants in BTNL2 and CHD risk through SNPStats online software. Genotyping of all candidate SNPs were completed by the Agena MassARRAY. In addition, we used false-positive report probability analysis to detect whether the positive findings were noteworthy observations. We also used Haploview 4.2 software and SNPStats online software to conduct the haplotype analysis and analysis of linkage disequilibrium (LD). Finally, the interaction of SNP-SNP in CHD risk was evaluated by multi-factor dimensionality reduction (MDR). Results: The results showed that BTNL2-rs35624343, -rs117896888, -rs41441651, -rs41417449, -rs28362680 and -rs2076523 were significantly associated with the CHD susceptibility. Especially for BTNL2-rs28362680, the allele A (OR = 0.68, p < 0.0001), genotype AA (OR = 0.40, p = 0.001) or GA (OR = 0.68, p < 0.0001) were associated with the reducing CHD risk. And -rs28362680 significantly reduced the CHD risk under all genetic models (dominant: OR = 0.64, p < 0.0001; recessive: OR = 0.47, p = 0.003; overdominant: OR = 0.73, p = 0.004; log-additive: OR = 0.66, p < 0.0001). And -rs28362680 was also closely associated with CHD risk reduction in all stratified analyses (age, gender, smoking, drinking, hypertension and diabetes). In addition, haplotype analysis showed that the "Crs117896888Crs41441651Trs41417449Ars28362680" (OR = 0.65, p < 0.0001) and "Grs117896888Trs41441651Crs41417449Ars28362680" (OR = 0.68, p = 0.013) may reduce CHD risk. Conclusion: Missense variants (rs35624343, rs117896888, rs41441651, rs41417449, rs28362680, rs2076523) may be protective factors for the CHD risk. In particular, there were sufficient evidences that BTNL2-rs28362680 can protective CHD risk.

High-efficiency NO electroreduction to NH3 over honeycomb carbon nanofiber at ambient conditions.

Electrocatalytic NO reduction is a promising technology for ambient NO removal with simultaneous production of highly value-added NH3. Herein, we report that honeycomb carbon nanofiber coated on carbon paper acts as an efficient metal-free catalyst for ambient electroreduction of NO to NH3. In 0.2 M Na2SO4 solution, such catalyst achieves an NH3 yield of 22.35 µmol h-1 cm-2 with a high Faradaic efficiency of up to 88.33%. Impressively, it also shows excellent stability for 10-h continuous electrolysis. Theoretical calculations reveal that the most active center of functional groups is -OH group for NO reduction with a low energy barrier (ΔG of 0.29 eV) for the potential-determining step (*NO + H → *HNO).