Computational Insights into the Photoinduced Dimeric Gold-Catalyzed Divergent Dechloroalkylation of gem-Dichloroalkanes with Alkenes.

The employment of dinuclear Au(I) catalysts in photomediated modern organic transformations has attracted significant attention over the past decade, which commonly demonstrates unique catalytic performance compared with the corresponding mononuclear gold complexes. Nevertheless, detailed mechanisms of dinuclear gold catalysis remain ambiguous, and further mechanistic understanding is highly desirable. Herein, computational studies were carried out to gain mechanistic insights into the photoinduced dinuclear gold-catalyzed divergent dechloroalkylation of gem-dichloroalkanes. Computational results suggest that a proton transfer from the additive, Hantzsch ester (HE), to the base, guanidine, could lead to an ionic pair complex, which is ready to undergo excitation under blue light irradiation to result in the corresponding triplet excited state. Then, the excited complex might undergo oxidative quenching with the dinuclear gold photocatalyst [AuI-AuI]2+, via a single-electron-transfer (SET) step to afford an unusual [Au1/2-Au1/2]+ dinuclear species. The corresponding mononuclear gold catalyst, [AuI]+, however, is not ready to enable the analogous step to give a [Au0] species, which might account for the unique characteristics of dinuclear gold catalysis. Subsequently, the formed [Au1/2-Au1/2]+ intermediate could trigger a Cl-atom transfer from dichloromethane in an inner-sphere manner to furnish a critical chloromethyl radical. Next, the resulting chloromethyl radical could attack the alkenyl moiety of substrates to generate the corresponding alkyl radicals. Then, three possible mechanistic pathways were explored to rationalize the substrate-dependent divergent transformations in this protocol. The main factors responsible for the diversified transformations were discussed.

One-Step Cooperative Growth of High Reaction Kinetics Composite Homogeneous Core-Shell Heterostructure.

Reaction kinetics can be improved by the enhanced electrical contact between different components growing symbiotically. But so far, due to the necessity for material synthesis conditions match, the component structures of cooperative growth are similar, and the materials are of the same type. The collaborative growth of high-reaction kinetics composite homogeneous core-shell heterostructure between various materials is innovatively proposed with different structures in one step. The NiCo-LDH and PPy successfully symbiotically grow on activated carbon fiber fabric in one step. The open channel structure of the NiCo-LDH nanosheets is preserved while PPy effectively wrapped around the NiCo-LDH. The well-defined nanostructure with abundant active sites and convenient ion diffusion paths is favorable for electrolyte entry into the entire nanoarrays. In addition, owing to the enhanced electronic interaction between different components through XPS analysis, the NiCo-LDH@PPy electrode shows outstanding reaction kinetics and structural stability. The as-synthesized NiCo-LDH@PPy exhibited excellent super-capacitive storage capabilities, robust capacitive activity, and good rate survival. Furthermore, an asymmetric supercapacitor (ASC) device made of NiCo-LDH@PPy and activated carbon (AC) is able to maintain a long cycle life while achieving high power and energy densities.

Circulating metabolites are associated with persistent elevations of ALT in patients with chronic hepatitis B with complete viral suppression.

Hepatitis B virus (HBV) can be completely suppressed after antiviral treatment; however, some patients with chronic hepatitis B (CHB) exhibit elevated alanine aminotransferase (ALT) levels and sustained disease progression. This study provides novel insights into the mechanism and potential predictive biomarkers of persistently elevated ALT (PeALT) in patients with CHB after complete viral inhibition. Patients having CHB with undetectable HBV DNA at least 12 months after antiviral treatment were enrolled from a prospective, observational cohort. Patients with PeALT and persistently normal ALT (PnALT) were matched 1:1 using propensity score matching. Correlations between plasma metabolites and the risk of elevated ALT were examined using multivariate logistic regression. A mouse model of carbon tetrachloride-induced liver injury was established to validate the effect of key differential metabolites on liver injury. Of the 1238 patients with CHB who achieved complete viral suppression, 40 (3.23%) had PeALT levels during follow-up (median follow-up: 2.42 years). Additionally, 40 patients with PnALT levels were matched as controls. Ser-Phe-Ala, Lys-Ala-Leu-Glu, 3-methylhippuric acid, 3-methylxanthine, and 7-methylxanthine were identified as critical differential metabolites between the two groups and independently associated with PeALT risk. Ser-Phe-Ala and Lys-Ala-Leu-Glu levels could be used to discriminate patients with PeALT from those with PnALT. Furthermore, N-acetyl- l-methionine (NALM) demonstrated the strongest negative correlation with ALT levels. NALM supplementation alleviated liver injury and hepatic necrosis induced by carbon tetrachloride in mice. Changes in circulating metabolites may contribute to PeALT levels in patients with CHB who have achieved complete viral suppression after antiviral treatment.

Alkali Extraction of Arsenic from Groundwater Treatment Sludge: An Essential Initial Step for Arsenic Recovery.

Arsenic (As)-bearing Fe(III) precipitate groundwater treatment sludge has traditionally been viewed by the water sector as a disposal issue rather than a resource opportunity, partly due to assumptions of the low value of As. However, As has now been classified as a Critical Raw Material (CRM) in many regions, providing new incentives to recover As and other useful components of the sludge, such as phosphate (P) and the reactive hydrous ferric oxide (HFO) sorbent. Here, we investigate alkali extraction to separate As from a variety of field and synthetic As-bearing HFO sludges, which is a critical first step to enable sludge upcycling. We found that As extraction was most effective using NaOH, with the As extraction efficiency increasing up to >99% with increasing NaOH concentrations (0.01, 0.1, and 1 M). Extraction with Na2CO3 and Ca(OH)2 was ineffective (<5%). Extraction time (hour, day, week) played a secondary role in As release but tended to be important at lower NaOH concentrations. Little difference in As extraction efficiency was observed for several key variables, including sludge aging time (50 days) and cosorbed oxyanions (e.g., Si, P). However, the presence of ∼10 mass% calcite decreased As release from field and synthetic sludges considerably (<70% As extracted). Concomitant with As release, alkali extraction promoted crystallization of poorly ordered HFO and decreased particle specific surface area, with structural modifications increasing with NaOH concentration and extraction time. Taken together, these results provide essential information to inform and optimize the design of resource recovery methods for As-bearing treatment sludge.

Effects of Training and Calibration Data on Surface Electromyogram-Based Recognition for Upper Limb Amputees.

Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.

Design, Synthesis, and Nematocidal Evaluation of Waltherione A Derivatives: Leveraging a Structural Simplification Strategy.

Southern root-knot nematodes are among the most pernicious phytoparasites; they are responsible for substantial yield losses in agricultural crops worldwide. The limited availability of nematicides for the prevention and control of plant-parasitic nematodes necessitates the urgent development of novel nematicides. Natural products have always been a key source for the discovery of pesticides. Waltherione A, an alkaloid, exhibits potent nematocidal activity. In this study, we designed and synthesized a series of quinoline and quinolone derivatives from Waltherione A, leveraging a strategy of structural simplification. Bioassays have revealed that the quinoline derivatives exhibit better activity than quinolone derivatives in terms of both nematocidal and fungicidal activities. Notably, compound D1 demonstrated strong nematocidal activity, with a 72 h LC50 of 23.06 µg/mL, and it effectively controlled the infection of root-knot nematodes on cucumbers. The structure-activity relationship suggests that the quinoline moiety is essential for the nematocidal efficacy of Waltherione A. Additionally, compound D1 exhibited broad-spectrum fungicidal activity, with an EC50 of 2.98 µg/mL against Botrytis cinerea. At a concentration of 200 µg/mL, it significantly inhibited the occurrence of B. cinerea on tomato fruits, with an inhibitory effect of 96.65%, which is slightly better than the positive control (90.30%).

Copper-Catalyzed Radical Allene C(sp2 )-H Cyanation.

While the hydrogen atom abstraction (HAA) from C(sp3 )-H bond has been well explored, the radical-mediated chemo- and regio-selective functionalization of allenic C(sp2 )-H bond via direct HAA from C(sp2 )-H bond of allene remains an unsolved challenge in synthetic chemistry. This is primarily due to inherent challenges with addition of radical intermediates to allenes, regioselectivity of HAA process, instability of allenyl radical toward propargyl radical et al. Herein, we report a copper catalyzed allenic C(sp2 )-H cyanation of an array of tri- and di-substituted allenes with exceptional site-selectivity, while mono-substituted allene was successfully cyanated, albeit with a low yield. In the developed strategy, steric N-fluoro-N-alkylsulfonamide, serving as precursor of hydrogen atom abstractor, plays a crucial role in achieving the desired regioselectivity and avoiding addition of N-centered radical to allene.

A robust soft sensor based on artificial neural network for monitoring microbial lipid fermentation processes using Yarrowia lipolytica.

Microbial oils produced by Yarrowia lipolytica offer an environmentally friendly and sustainable alternative to petroleum as well as traditional lipids from animals and plants. The accurate measurement of fermentation parameters, including the substrate concentration, dry cell weight, and lipid accumulation, is the foundation of process control, which is indispensable for industrial lipid production. However, it remains a great challenge to measure the complex parameters online during the lipid fermentation process, which is nonlinear, multivariate, and characterized by strong coupling. As a type of AI technology, the artificial neural network model is a powerful tool for handling extremely complex problems, and it can be employed to develop a soft sensor to monitor the microbial lipid fermentation process of Y. lipolytica. In this study, we first analyzed and emphasized the volume of sodium hydroxide and dissolved oxygen concentration as central parameters of the fermentation process. Then, a soft sensor based on a four-input artificial neural network model was developed, in which the input variables were fermentation time, dissolved oxygen concentration, initial glucose concentration, and additional volume of sodium hydroxide. This provides the possibility of online monitoring of dry cell weight, glucose concentration, and lipid production with high accuracy, which can be extended to similar fermentation processes characterized by the addition of bases or acids, as well as changes of the dissolved oxygen concentration.

Computational Understanding of Dual Gold and Photoredox-Catalyzed Regioselective Thiosulfonylation of Alkenes.

Herein, a computational work was carried out to gain mechanistic insights into dual gold and photoredox-catalyzed regioselective thiosulfonylation of alkenes with PhSO2SCF3. Computational results suggest that it is more favorable for the complex of Au(I) with PhSO2SCF3 (INT1), instead of an Au(I) catalyst or individual substrates, to quench the excited *[Ru]II photocatalyst in a single-electron oxidative manner to afford [Ru]III. The complexation of the Au(I) catalyst with PhSO2SCF3 could lead to a substantially lowered energy level of the lowest unoccupied molecular orbital, which may be mainly responsible for the feasibility of INT1 in quenching the excited photocatalyst. The resultant single-electron reduced complex, subsequently, is ready to undergo a S-S bond cleavage to form an Au(I)-SCF3 species and a benzenesulfonyl radical. Next, the yielded Au(I)-SCF3 species could undergo single-electron oxidation by [Ru]III to afford an Au(II) intermediate. Subsequently, the binding with an alkyl radical for the formed Au(II) species could occur to further convert to an Au(III) species, from which the final product can be furnished by a reductive elimination step and the Au(I) catalyst is regenerated. Thus, an Au(I)/Au(II)/Au(III)/Au(I) catalytic cycle is suggested to mainly account for the regioselective thiosulfonylation of alkenes.

Enhancing Electrocatalytic Hydrodechlorination through Interfacial Microenvironment Modulation.

Electrochemical reduction (ER) is a promising approach to safely remove pollutants. However, sluggish reaction kinetics and significant side reactions considerably limit the applicability of this green process. Herein, we uncovered the previously ignored role of interfacial hydrophilicity in determining the ER performance through electron microscopy observations, contact angle (CA) analysis, and electrochemical measurements. A Pd/C electrocatalyst forms dense nanopores on the electrode surface, rendering it highly hydrophobic and achieving a CA of up to 145°. This imposes a large mass-transfer barrier for the diffusion of water and pollutants into Pd sites. Moreover, the release of H2 is suppressed, which changes the solid-liquid (Pd-polluted water) interface into a solid-gas (H2)-liquid interface. This further slows down mass transfer and the decontamination process. This dilemma can be easily alleviated by adding hydrophilic polymers like polyethylene glycol to increase hydrophilicity and improve mass transfer. By this way, the activity and Faraday efficiency of Pd/C in the electrochemical hydrodehalogenation of 2,4-dichlorophenol could be increased by 4-5 times. Moreover, this interfacial microenvironment modulation strategy is parallel to other approaches, such as Pd structural engineering, and therefore these strategies can be combined to further increase the electrochemical decontamination performance of electrocatalysts.