Predicting and understanding photocatalytic CO2 reduction reaction with IR spectroscopy-based interpretable machine learning framework.

The highly selective conversion of carbon dioxide into value-added products is extremely valuable. However, even with the aid of in situ characterization techniques, it remains challenging to directly correlate extensive spectral data carrying microscopic information with macroscopic performance. Herein, we adopted advanced machine learning (ML) approaches to establish an accurate and interpretable relationship between vibrational spectral signals and catalytic performances to uncover hidden physical insights. Focusing on photocatalytic CO2 reduction, our model is shown to effectively and accurately predict the CO production activity and selectivity based solely on the infrared (IR) spectral signals, the generalizability of which is additionally demonstrated with a new Bi5O7I photocatalytic system. More importantly, further model analysis has revealed a novel strategy to steer CO selectivity, the physical sanity of which is verified by a detailed reaction mechanism analysis. This work demonstrates the tremendous potential of machine-learned spectroscopy to efficiently identify reaction control factors, which can further lay the foundation for targeted optimization and reverse design.

Accelerating acidic CO2 electroreduction: strategies beyond catalysts.

Carbon dioxide electrochemical reduction (CO2RR) into high-value-added chemicals offers an alternative pathway toward achieving carbon neutrality. However, in conventional neutral or alkaline electrolyte systems, a significant portion of CO2 is converted into (bi)carbonate due to the thermodynamically favorable acid-base neutralization reaction between CO2 and hydroxide ions. This results in the single-pass carbon efficiency (SPCE) being theoretically capped at 50%, presenting challenges for practical applications. Acidic CO2RR can completely circumvent the carbonate issue and theoretically achieve 100% SPCE, garnering substantial attention from researchers in recent years. Nevertheless, acidic CO2RR currently lags behind traditional neutral/alkaline systems in terms of product selectivity, stability, and energy efficiency, primarily because the abundance of H+ ions exacerbates the hydrogen evolution reaction (HER). Encouragingly, significant breakthroughs have been made to address these challenges, with numerous studies indicating that the regulation of the local catalytic environment may be more crucial than the catalyst itself. In this review, we will discuss the main challenges and latest strategies for acidic CO2RR, focusing on three key aspects beyond the catalyst: electrolyte regulation, local catalytic environment modification, and novel designs of gas diffusion electrodes (GDEs)/electrolyzers. We will also conclude the current advancement for acidic CO2RR and provide an outlook, with the hope that this technology will contribute to achieving carbon neutrality and advance towards practical application.

The role of inflammatory proteins in regulating the impact of lipid specifications on deep venous thrombosis: a two sample and mediated Mendelian randomization study.

Objective: To investigate the potential mediating role of inflammatory proteins in the association between lipid species and Deep Venous Thrombosis (DVT). Methods: A comprehensive analysis was conducted using pooled data from genome-wide association studies (GWAS), incorporating double-sample and reverse Mendelian randomization (MR) techniques, to identify the specific inflammatory proteins that act as intermediaries among 91 screened proteins in relation to deep vein thrombosis (DVT). Furthermore, a two-step MR approach was employed to quantify the proportion of DVT risk attributed to lipid effects mediated by these inflammatory proteins. Results: The MR Analysis revealed that the two inflammatory proteins, as predicted by genetics, served as mediating factors in the impact of five lipids on DVT. No reverse effect of DVT was observed on 179 lipid species and 91 inflammatory proteins. In the case of TAG(58:7) and its influence on DVT, CCL20 played an intermediary role with an estimated proportion of 12.51% (ranging from 12% to 13%). SIRT2 exhibited a masking effect on DVT for PC(17:0/20:4) and PC(18:0/20:4), while CCL20 masked the impact of DVT on PC(14:0/18:2), PC(15:0/18:2), and PC(18:0/20:5). Conclusions: In our study, we identified CCL20 as a crucial mediator in the association between TAG(58:7) and DVT, with a mediating proportion of 12.51% (12%-13%). Further investigations are warranted to explore other potential risk factors acting as mediators.

Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.

The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effective, suffer from being costly and time-consuming. In response to this challenge, new alternative methodologies, notably machine learning and deep learning techniques, have attracted attention for their potential in developing carcinogenicity prediction models. This article reviews the progress in predicting carcinogenicity using various machine learning and deep learning algorithms. A comparative analysis on these developed models reveals that support vector machine, random forest, and ensemble learning are commonly preferred for their robustness and effectiveness in predicting chemical carcinogenicity. Conversely, models based on deep learning algorithms, such as feedforward neural network, convolutional neural network, graph convolutional neural network, capsule neural network, and hybrid neural networks, exhibit promising capabilities but are limited by the size of available carcinogenicity datasets. This review provides a comprehensive analysis of current machine learning and deep learning models for carcinogenicity prediction, underscoring the importance of high-quality and large datasets. These observations are anticipated to catalyze future advancements in developing effective and generalizable machine learning and deep learning models for predicting chemical carcinogenicity.

Surface Defect-Induced Specific Catalysis Activates 100% Selective Sensing toward Amine Gases at Room Temperature.

Achieving selective sensing toward target volatile organic compound gases is of vital importance in the fields of air quality assessment, food freshness evaluation, and diagnosis of patients via exhaled breath. However, chemiresistive sensors that exhibit specificity like biological enzymes in a complex environment are rare. Herein, we developed a strategy of optimizing oxygen vacancy structures in tin oxides to induce specific catalysis, activating 100% selective sensing toward amine gases at room temperature. In situ technologies and theoretical calculations reveal that the "donor-receptor" coordination between nitrogen atoms from amine molecules and bridging oxygen vacancies (OVBri)-induced electron-deficient center is the essence of specific catalysis and provides the bridge from the surface oxidation reaction to electrophysical characteristics evolution, which allows the sensor to exhibit amine-specific sensing behavior, even in gas mixtures. Moreover, OVBri enhances the selectivity by enabling a room-temperature sensing pathway where lattice oxygens participate in catalytic oxidation for amine molecules, resulting in record-high sensing values: 19,938.92 toward 100 ppm of triethylamine, 15,236.78 toward trimethylamine, and 123.41 toward diethylamine. Our findings illustrate the feasibility of designing specific active sites through defect engineering and can contribute to the advancement of highly selective sensors based on catalytic processes.

Strong Dipole Moments from Ferrocene Methanol Clusters Boost Exciton Dissociation in Quantum-Confined Perovskite for CO2 Photoreduction.

Perovskite nanocrystals (PCNs) exhibit a significant quantum confinement effect that enhances multiexciton generation, making them promising for photocatalytic CO2 reduction. However, their conversion efficiency is hindered by poor exciton dissociation. To address this, we synthesized ferrocene-methanol-functionalized CsPbBr3 (CPB/FcMeOH) using a ligand engineering approach. By manipulating the electronic coupling between ligands and the PCN surface, facilitated by the increased dipole moment from hydrogen bonding in FcMeOH molecules, we effectively controlled exciton dissociation and interfacial charge transfer. Under 5 h of irradiation, the CO yield of CPB/FcMeOH reached 772.79 µmol g-1, 4.95 times higher than pristine CPB. This high activity is due to the formation of hydrogen-bonded FcMeOH clusters on the CPB surface. The nonpolar disruption and strong dipole moment of FcMeOH molecules enhance electronic coupling between the FcMeOH ligands and the CPB surface, reducing the surface barrier energy. Consequently, exciton dissociation and interfacial charge transfer are promoted, efficiently utilizing multiple excitons in quantum-confined domains. Transient absorption spectroscopy confirms that CPB/FcMeOH exhibits optimized exciton behavior with fast internal relaxation, trapping, and a short recombination time, allowing photogenerated charges to more rapidly participate in CO2 reduction.

Dissecting the Photochemical Reactivity of Metal Ions during Atmospheric Nitrate Transformations on Photoactive Mineral Dust.

Dissecting the photochemical reactivity of metal ions is a significant contribution to understanding secondary pollutant formation, as they have a role to be reckoned with atmospheric chemistry. However, their photochemical reactivity has received limited attention within the active nitrogen cycle, particularly at the gas-solid interface. In this study, we delve into the contribution of magnesium ion (Mg2+) and ferric ion (Fe3+) to nitrate decomposition on the surface of photoactive mineral dust. Under simulated sunlight irradiation, the observed NOX production rate differs by an order of magnitude in the presence of Mg2+ (6.02 × 10-10 mol s-1) and Fe3+ (2.07 × 10-11 mol s-1). The markedly decreased fluorescence lifetime induced by Mg2+ and the change in the valence of Fe3+ revealed that Mg2+ and Fe3+ significantly affect the concentration of nitrate decomposition products by distinct photochemical reactivity with photogenerated electrons. Mg2+ promotes NOX production by accelerating charge transfer, while Fe3+ hinders nitrate decomposition by engaging in a redox cyclic reaction with Fe2+ to consume photogenerated carriers continuously. Furthermore, when Fe3+ coexists with other metal ions (e.g., Mg2+, Ca2+, Na+, and K+) and surpasses a proportion of approximately 12%, the photochemical reactivity of Fe3+ tends to be dominant in depleting photogenerated electrons and suppressing nitrate decomposition. Conversely, below this threshold, the released NOX concentration increases sharply as the proportion of Fe3+ decreases. This research offers valuable insights into the role of metal ions in nitrate transformation and the generation of reactive nitrogen species, contributing to a deep understanding of atmospheric photochemical reactions.

Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study.

BACKGROUND: Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identifying cardiac safety issues. Various QSAR models exist, but their performance varies. Ongoing improvements show promise, necessitating continued efforts to enhance accuracy using emerging deep learning algorithms in predicting potential hERG blockade. STUDY DESIGN AND METHOD: Using a large training dataset, six individual QSAR models were developed. Additionally, three ensemble models were constructed. All models were evaluated using 10-fold cross-validations and two external datasets. RESULTS: The 10-fold cross-validations resulted in Mathews correlation coefficient (MCC) values from 0.682 to 0.730, surpassing the best-reported model on the same dataset (0.689). External validations yielded MCC values from 0.520 to 0.715 for the first dataset, exceeding those of previously reported models (0-0.599). For the second dataset, MCC values fell between 0.025 and 0.215, aligning with those of reported models (0.112-0.220). CONCLUSIONS: The developed models can assist the pharmaceutical industry and regulatory agencies in predicting hERG blockage activity, thereby enhancing safety assessments and reducing the risk of adverse cardiac events associated with new drug candidates.

Photocatalytic Formamide Synthesis via Coupling of Electrophilic and Nucleophilic Radicals over Atomically Dispersed Bi Sites.

Formamide (HCONH2) plays a pivotal role in the manufacture of a diverse array of chemicals, fertilizers, and pharmaceuticals. Photocatalysis holds great promise for green fabrication of carbon-nitrogen (C-N) compounds owing to its environmental friendliness and mild redox capability. However, the selective formation of the C-N bond presents a significant challenge in the photocatalytic synthesis of C-N compounds. This work developed a photocatalytic radical coupling method for the formamide synthesis from co-oxidation of ammonia (NH3) and methanol (CH3OH). An exceptional formamide yield rate of 5.47±0.03 mmol ⋅ gcat -1 ⋅ h-1 (911.87±5 mmol ⋅ gBi -1 ⋅ h-1) was achieved over atomically dispersed Bi sites (BiSAs) on TiO2. An accumulation of 45.68 mmol ⋅ gcat -1 (2.0 g ⋅ gcat -1) of formamide was achieved after long-term illumination, representing the highest level of photocatalytic C-N compounds synthesis. The critical C-N coupling for formamide formation originated from the "σ-σ" interaction between electrophilic ⋅CH2OH with nucleophilic ⋅NH2 radical. The BiSAs sites facilitated the electron transfer between reactants and photocatalysts and enhanced the nucleophilic attack of ⋅NH2 radical on the ⋅CH2OH radical, thereby advancing the selective C-N bond formation. This work deepens the understanding of the C-N coupling mechanism and offers an intriguing photocatalytic approach for the efficient and sustainable production of C-N compounds.

Harnessing Dual Phototherapy and Immune Activation for Cancer Treatment: The Development and Application of BODIPY@F127 Nanoparticles.

Conventional phototherapeutic agents are typically used in either photodynamic therapy (PDT) or photothermal therapy (PTT). However, efficacy is often hindered by hypoxia and elevated levels of heat shock proteins in the tumor microenvironment (TME). To address these limitations, a formylated, near-infrared (NIR)-absorbing and heavy-atom-free Aza-BODIPY dye is presented that exhibits both type-I and type-II PDT actions with a high yield of reactive oxygen species (ROS) and manifests efficient photothermal conversion by precise adjustments to the conjugate structure and electron distribution, leading to a large amount of ROS production even under severe hypoxia. To improve biosafety and water solubility, the dye with an amphiphilic triblock copolymer (Pluronic F-127), yielding BDP-6@F127 nanoparticles (NPs) is coated. Furthermore, inspired by the fact that phototherapy triggers the release of tumor-associated antigens, a strategy that leverages potential immune activation by combining PDT/PTT with immune checkpoint blockade (ICB) therapy to amplify the systemic immune response and achieve the much-desired abscopal effect is developed. In conclusion, this study presents a promising molecular design strategy that integrates multimodal therapeutics for a precise and effective approach to cancer therapy.

Bilateral Embedded Anchoring via Tailored Polymer Brush for Large-Area Air-processed Blue Light-Emitting Diodes.

Perovskite light-emitting diodes (PeLEDs) that can be air-processed promises the development of displaying optoelectronic device, while is challenged by technical difficulty on both the active layer and hole transport layer (HTL) caused by the unavoidable humidity interference. Here, we propose and validate that, planting the polymer brush with tailored functional groups in inorganic HTL, provides unique bilateral embedded anchoring that is capable of simultaneously addressing the n phases crystallization rates in the active layer as well as the deteriorated particulate surface defects in HTL. Exemplified by zwitterionic polyethyleneimine-sulfonate (PEIS) in present study, its implanting in NiOx HTL offers abundant nuclei sites of amino and sulfonate groups that balance the growth rate of different n phases in quasi-2D perovskite films. Moreover, the PEIS effectively nailed the interfacial contact between perovskite and NiOx, and reduced the particulate surface defects in HTL, leading to the enhanced PLQY and stability of large-area blue perovskite film in ambient air. By virtue of these merits, present work achieves the first demonstration of the air-processed blue PeLEDs in large emitting area of 1.0 cm2 with peak external quantum efficiency (EQE) of 2.09 %, which is comparable to the similar pure-bromide blue PeLEDs fabricated in glovebox.

Resolving the overlooked photochemical nitrophenol transformation mechanism induced by nonradical species under visible light.

Nitrophenols present on the surface of particulates are ubiquitous in the atmosphere. However, its atmospheric photochemical transformation pathway remains unknown, for which the crucial effect of visible light is largely overlooked, resulting in an incomplete understanding of the effects of nitrophenols in the atmospheric environment. This study delves into the photolysis mechanism of 4-nitrophenol (4NP), one of the most abundant atmospheric nitrophenol compounds, on the surface of photoactive particulates under visible light irradiation. Unexpectedly, the nonradical species (singlet oxygen, 1O2) was identified as a dominant factor in driving the visible photolysis of 4NP. The pathways of HONO and p-benzoquinone (C6H4O2) generation were clarified by acquiring direct evidence of C-N and O-H bond breakage in the nitro (-NO2) and hydroxyl (-OH) groups of 4NP. The further decomposition of HONO results in the generation of NO and hydroxyl radicals, which could directly contribute to atmospheric oxidizing capacity and complicate the PM2.5 composition. Significantly, the behavior of 1O2-induced visible photolysis of 4NP was universal on the surface of common particulates in the atmosphere, such as A1 dust and Fe2O3. This work advances the understanding of the photochemical transformation mechanism of particulate-phase atmospheric nitrophenols, which is indispensable in elucidating the role of nitrophenols in atmospheric chemistry.

Steering Photooxidation of Methane to Formic Acid over A Priori Screened Supported Catalysts.

Efficient methane photooxidation to formic acid (HCOOH) has emerged as a sustainable approach to simultaneously generate value-added chemicals and harness renewable energy. However, the persistent challenge lies in achieving a high yield and selectivity for HCOOH formation, primarily due to the complexities associated with modulating intermediate conversion and desorption after methane activation. In this study, we employ first-principles calculations as a comprehensive guiding tool and discover that by precisely controlling the O2 activation process on noble metal cocatalysts and the adsorption strength of carbon-containing intermediates on metal oxide supports, one can finely tune the selectivity of methane photooxidation products. Specifically, a bifunctional catalyst comprising Pd nanoparticles and monoclinic WO3 (Pd/WO3) would possess optimal O2 activation kinetics and an intermediate oxidation/desorption barrier, thereby promoting HCOOH formation. As evidenced by experiments, the Pd/WO3 catalyst achieves an exceptional HCOOH yield of 4.67 mmol gcat-1 h-1 with a high selectivity of 62% under full-spectrum light irradiation at room temperature using molecular O2. Notably, these results significantly outperform the state-of-the-art photocatalytic systems operated under identical condition.

In situ fabrication of atomically adjacent dual-vacancy sites for nearly 100% selective CH4 production.

The photocatalytic CO2-to-CH4 conversion involves multiple consecutive proton-electron coupling transfer processes. Achieving high CH4 selectivity with satisfactory conversion efficiency remains challenging since the inefficient proton and electron delivery path results in sluggish proton-electron transfer kinetics. Herein, we propose the fabrication of atomically adjacent anion-cation vacancy as paired redox active sites that could maximally promote the proton- and electron-donating efficiency to simultaneously enhance the oxidation and reduction half-reactions, achieving higher photocatalytic CO2 reduction activity and CH4 selectivity. Taking TiO2 as a photocatalyst prototype, the operando electron paramagnetic resonance spectra, quasi in situ X-ray photoelectron spectroscopy measurements, and high-angle annular dark-field-scanning transmission electron microscopy image analysis prove that the VTi on TiO2 as initial sites can induce electron redistribution and facilitate the escape of the adjacent oxygen atom, thereby triggering the dynamic creation of atomically adjacent dual-vacancy sites during photocatalytic reactions. The dual-vacancy sites not only promote the proton- and electron-donating efficiency for CO2 activation and protonation but also modulate the coordination modes of surface-bound intermediate species, thus converting the endoergic protonation step to an exoergic reaction process and steering the CO2 reduction pathway toward CH4 production. As a result, these in situ created dual active sites enable nearly 100% CH4 selectivity and evolution rate of 19.4 µmol g-1 h-1, about 80 times higher than that of pristine TiO2. Thus, these insights into vacancy dynamics and structure-function relationship are valuable to atomic understanding and catalyst design for achieving highly selective catalysis.

Characterization of phosphate modified red mud-based composite materials and study on heavy metal adsorption.

In this paper, Bayer red mud (RM) and lotus leaf powder (LL) were used as the main materials, and KH2PO4 was added to modify the material. Under the condition of high-temperature carbonization, RMLL was prepared and phosphate modified red mud matrix composite (PRMLL) was prepared based on KH2PO4 modification, which can effectively remove Pb2+ from water. The optimum preparation and application conditions were determined through orthogonal experiment: dosage 0.1g, ratio 1:1, and temperature 600 °C. The effects of pH, dosage, and initial concentration on the adsorption of Pb2+ were studied. The pseudo-first-order, pseudo-second-order, and Elovich kinetic models were fitted to the experimental data. It was found that RMLL and PRMLL were more consistent with the pseudo-second-order kinetic model and chemisorption. Langmuir, Freundlich, Timkin, and Dubinin-Radushkevich isothermal adsorption models were used to fit the experimental data. It was found that RMLL and PRMLL were more consistent with Langmuir model. In addition, the maximum adsorption capacity of RMLL and PRMLL was 188.1 mg/g and 213.4 mg/g, respectively. It is larger than the adsorption capacity of their monomers. Therefore, the use of RMLL and PRMLL as the removal of Pb2+ from water is a potential application material.

Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

The relationship between innate/adaptive immunity and gastrointestinal cancer : a multi-omics Mendelian randomization study.

BACKGROUND: Innate/adaptive immunity is the key to anti-tumor therapy. However, its causal relationship to Gastrointestinal (GI) cancer remains unclear. METHODS: Immunity genes were extracted from the MSigDB database. The Genome-wide association studies (GWAS) summary data of GI cancer were integrated with expression quantitative trait loci (eQTL) and DNA methylation quantitative trait loci (mQTL) associated with genes. Summary-data-based Mendelian randomization (SMR) and co-localization analysis were used to reveal causal relationships between genes and GI cancer. Two-sample MR analysis was used for sensitivity analysis. Single cell analysis clarified the enrichment of genes. RESULTS: Three-step SMR analysis showed that a putative mechanism, cg17294865 CpG site regulating HLA-DRA expression was negatively associated with gastric cancer risk. HLA-DRA was significantly differentially expressed in monocyte/macrophage and myeloid cells in gastric cancer. CONCLUSION: This study provides evidence that upregulating the expression level of HLA-DRA can reduce the risk of gastric cancer.

Photocatalytic NO x removal and recovery: progress, challenges and future perspectives.

The excessive production of nitrogen oxides (NO x ) from energy production, agricultural activities, transportation, and other human activities remains a pressing issue in atmospheric environment management. NO x serves both as a significant pollutant and a potential feedstock for energy carriers. Photocatalytic technology for NO x removal and recovery has received widespread attention and has experienced rapid development in recent years owing to its environmental friendliness, mild reaction conditions, and high efficiency. This review systematically summarizes the recent advances in photocatalytic removal, encompassing NO x oxidation removal (including single and synergistic removal and NO3 - decomposition), NO x reduction to N2, and the emergent NO x upcycling into green ammonia. Special focus is given to the molecular understanding of the interfacial nitrogen-associated reaction mechanisms and their regulation pathways. Finally, the status and the challenges of photocatalytic NO x removal and recovery are critically discussed and future outlooks are proposed for their potential practical application.

Efficient and Durable Oxidation Removal of Formaldehyde over Layered Double Hydroxide Catalysts at Room Temperature.

Room temperature catalytic oxidation (RTCO) using non-noble metals has emerged as a highly promising technique for removal of formaldehyde (HCHO) under ambient conditions; however, non-noble catalysts still face the challenges related to poor water resistance and low stability under harsh conditions. In this study, we synthesized a series of layered double hydroxides (LDHs) incorporating various dual metals (MgAl, ZnAl, NiAl, NiFe, and NiTi) for formaldehyde oxidation at ambient temperature. Among the synthesized catalysts, the NiTi-LDH catalyst showed an HCHO removal efficiency and CO2 yield close to 100.0%, and exceptional water resistance and chemical stability on running 1300 min. The abundant hydroxyl groups in LDHs directly bonded with HCHO, leading to the production of CO2 and H2O, thus inhibiting the formation of CO, even in the absence of O2 and H2O. The coexistence of O2 effectively reduced the reaction barrier for H2O molecule dissociation, facilitating the formation of hydroxyl groups and their subsequent backfill on the catalyst surface. The mechanisms underlying the involvement and regeneration of hydroxyl groups in room temperature oxidation of formaldehyde were elucidated with the combined in situ DRIFTS, HCHO-TPD-MS, and DFT calculations. This work not only demonstrates the potential of LDH catalysts in environmental applications but also advances the understanding of the fundamental processes involved in room temperature oxidation of formaldehyde.