Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms.

The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimization approaches are poorly suited to multivariable multimetric photocatalytic systems because they aim to optimize one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multimetric challenge by defining a metric for holistic system performance that takes multiple figures of merit into account, and employ a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic optimization accessible for human experimentalists. As a test platform, we employ a five-component system that self-assembles into photocatalytic micelles for CO2-to-CO reduction, which we experimentally optimized to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the data set with machine learning algorithms allows quantification of each parameter's effect on overall system performance. The buffer concentration is unexpectedly revealed as the dominating parameter for optimal photocatalytic activity, and is nearly four times more important than the catalyst concentration. The expanded use and standardization of this methodology to define and optimize holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit.

Efficient Electron Transfer Driven by Excited-State Structural Relaxation in Corrole-Perylenedimiide Dyad.

A sterically encumbered trans-A2B-corrole possessing a perylenediimide (PDI) scaffold in close proximity to the macrocycle has been synthesized via a straightforward route. Electronic communication as probed via steady-state absorption or cyclic voltammetry is weak in the ground state, in spite of the corrole ring and PDI being bridged by an o-phenylene unit. The TDDFT excited-state geometry optimization suggests after excitation the interchromophoric distance is markedly reduced, thus enhancing the through-space electronic coupling between the corrole and the PDI. This is corroborated by the strong deviation of the emission spectrum originating from both PDI and corrole in the dyad. Selective excitation of both donor and acceptor units triggers efficient sub-picosecond electron transfer and hole transfer, respectively, followed by fast charge recombination. In comparison to previously studied corrole-PDI dyads, both charge separation and charge recombination occur faster, because of the structural relaxation in the excited state.

Probing the activated complex of the F + NH3 reaction via a dipole-bound state.

Experimental characterization of the transition state poses a significant challenge due to its fleeting nature. Negative ion photodetachment offers a unique tool for probing transition states and their vicinity. However, this approach is usually limited to Franck-Condon regions. For example, high-lying Feshbach resonances with an excited HF stretching mode (vHF = 2-4) were recently identified in the transition-state region of the F + NH3 → HF + NH2 reaction through photo-detaching FNH3- anions, but the direct photodetachment failed to observe the lower-lying vHF = 0,1 resonances and bound states due apparently to negligible Franck-Condon factors. Indeed, these weak transitions can be resonantly enhanced via a dipole-bound state (DBS) formed between an electron and the polar FNH3 species. In this study, we unveil a series of Feshbach resonances and bound states along the F + NH3 reaction path via a DBS by combining high-resolution photoelectron spectroscopy with high-level quantum dynamical computations. This study presents an approach for probing the activated complex in a reaction by negative ion photodetachment through a DBS.

Analysis of the retraction papers in oncology field from Chinese scholars from 2013 to 2022.

OBJECTIVE: To analyze the characteristics of retracted oncology papers from Chinese scholars and the reasons for retraction. METHODS: Data on retracted oncology papers from Chinese scholars published from 2013 to 2022 were retrieved from the Retraction Watch database. The retraction number and annual distribution, article types, reasons for retraction, retraction time delay, publishers, and journal characteristics of the retracted papers were analyzed. RESULTS: A total of 2695 oncology papers from Chinese scholars published from 2013 to 2022 had been retracted. The majority of these papers were published from 2017 to 2020. In terms of article type, 2538 of the retracted papers were research articles, accounting for 94.17% of the total number of retracted papers. The main reasons for retraction were data, result, and image problems, duplicate publication, paper mills, author- and third-party-related reasons, plagiarism, false reviews, and method errors. The retraction time delay for the retracted papers ranged from 0 to 3582 days (median, 826 days). The retractions mainly occurred within the first 4 years after publication. A total of 77 publishers were involved in the retracted papers. In terms of journal distribution, 394 journals were involved in the retracted papers, of which 368 (93.40%) were included in the SCI database. There were 243 journals with an impact factor of <5 (66.03%). CONCLUSION: In the field of oncology, the annual distribution of retracted papers from Chinese scholars exhibited first an increasing and subsequently a decreasing trend, reaching a peak in 2019, indicating an improvement in the status of retraction after 2021. The main type of the retracted papers was research article, and the main reason for retraction was academic misconduct. The retractions were mainly concentrated in several major publishers and periodicals in Europe and the United States. Most of the journals had low-impact factors.

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning.

Calculating the thermal rate constants of elementary combustion reactions is of great importance in theoretical chemistry. Machine learning has become a powerful, data-driven method for predicting rate constants nowadays. Recently, the molecular similarity combined with the topological indices were proposed to represent the hydrogen abstraction reactions of alkane [J. Chem. Inf. Model. 2023, 63, 5097-5106], which are, however, not applicable to alkane cracking reactions, another important class of combustion reactions, due to the cleavage of the C-C bond. In this work, a new feature selection scheme is proposed to describe both bimolecular and unimolecular cracking reactions. Molecular descriptors are elaborately selected individually for both reactants and products from those generated by the open-source software RDKit. Machine learning models combined with these molecular descriptors are proven to have the ability to accurately predict rate constants of both the hydrogen abstraction reactions of alkanes by CH3 and the alkane cracking reactions. The average deviation of the XGB-FNN model for prediction is around 60% for the hydrogen abstraction reactions of alkanes and 100% for the alkane cracking reactions. It is expected that the descriptors proposed in this work can be applied to build machine learning models for other reactions.

NIR-Triggered Multifunctional NO Nanoplatform for Conquering Thermal Resistance in Biofilms.

Photothermal treatment (PTT) has emerged as a promising avenue for biofilm elimination, yet its potential drawbacks, such as local hyperpyrexia and bacterial heat resistance, have posed challenges. To address these concerns, an innovative nanoplatform (Au@mSiO2 -arg/ICG) is devised that integrates phototherapeutic and gas therapeutic functionalities. This multifaceted nanoplatform is composed of mesoporous silica-coated Au nanorods (Au@mSiO2 ), supplemented with l-arginine (l-arg) and indocyanine green (ICG), and is engineered for mild temperature PTT aimed at biofilm eradication. Au@mSiO2 -arg/ICG nanoparticles (NPs) show excellent antibacterial effects through the generation of nitric oxide (NO) gas, heat, and reactive oxygen species (ROS) under 808 nm light irradiation. The ROS generated by ICG initiates a cascade reaction with l-arg, ultimately yielding NO gas molecules. This localized release of NO not only effectively curbs the expression of heat shock proteins 70 mitigating bacterial thermoresistance, but also reduces extracellular polymeric substance allowing better penetration of the therapeutic agents. Furthermore, this nanoplatform achieves an outstanding biofilm elimination rate of over 99% in an abscess model under 808 nm light irradiation (0.8 W·cm-2 ), thereby establishing its potential as a dependable strategy for NO-enhanced mild PTT and antibacterial photodynamic therapy (aPDT) in clinical settings.

Chlorophyl-Passivated Ytterbium-Doped Perovskite Quantum-Cutting Film for High-Performance Solar Energy Conversion and Near-Infrared Light-Emitting Diode Applications.

The quantum cutting ytterbium (Yb3+)-doped CsPbX3 (X = Cl, Cl, or Br) nanocrystals, exhibiting photoluminescence quantum yields (PLQYs) exceeding 100%, hold significant promise for applications in solar energy conversion technologies and near-infrared (NIR) light-emitting diodes (LEDs). This work investigates the usage of chlorophyll (CHL), a naturally existing organic pigment, as an efficient molecular passivator to improve the performance of quantum cutting films. With the assistance of CHL, the resultant perovskite film displays an increased PLQY of 176%. The commercial silicon solar cells (SSCs) with CHL-treated perovskite films demonstrate a remarkable photon-to-current conversion efficiency improvement of 1.83% for a 330.15 cm2 area SSC device. Additionally, a CHL-modified Yb3+:CsPbCl3 film was used to create 988 nm NIR LEDs with an external quantum efficiency of 3.2%. This work provides a new, eco-friendly approach for producing high-quality, large-area Yb3+-doped perovskite film for deployment in photoelectric and night vision applications.

Recent advancements with loop-mediated isothermal amplification (LAMP) in assessment of the species authenticity with meat and seafood products.

Species adulteration or mislabeling with meat and seafood products could negatively affect the fair trade, wildlife conservation, food safety, religion aspect, and even the public health. While PCR-based methods remain the gold standard for assessment of the species authenticity, there is an urgent need for alternative testing platforms that are rapid, accurate, simple, and portable. Owing to its ease of use, low cost, and rapidity, LAMP is becoming increasingly used method in food analysis for detecting species adulteration or mislabeling. In this review, we outline how the features of LAMP have been leveraged for species authentication test with meat and seafood products. Meanwhile, as the trend of LAMP detection is simple, rapid and instrument-free, it is of great necessity to carry out end-point visual detection, and the principles of various end-point colorimetry methods are also reviewed. Moreover, with the aim to enhance the LAMP reaction, different strategies are summarized to either suppress the nonspecific amplification, or to avoid the results of nonspecific amplification. Finally, microfluidic chip is a promising point-of-care method, which has been the subject of a great deal of research directed toward the development of microfluidic platforms-based LAMP systems for the species authenticity with meat and seafood products.

Realizing Symmetry-Breaking Architectures in Soap Films.

We show here that soap films-typically expected to host symmetric molecular arrangements-can be constructed with differing opposite surfaces, breaking their symmetry, and making them reminiscent of functional biological motifs found in nature. Using fluorescent molecular probes as dopants on different sides of the film, resonance energy transfer could be employed to confirm the lack of symmetry, which was found to persist on timescales of several minutes. Further, a theoretical analysis of the main transport phenomena involved yielded good agreement with the experimental observations.

High-Performance Blue Perovskite Light-Emitting Diodes Enabled by Synergistic Effect of Additives.

While quasi-two-dimensional (quasi-2D) perovskites have good properties of cascade energy transfer, high exciton binding energy, and high quantum efficiency, which will benefit high-efficiency blue PeLEDs, inefficient domain distribution management and unbalanced carrier transport impede device performance improvement. Herein, (2-(9H-carbazol-9-yl)ethyl)phosphonic acid (2PACz) and methyl 2-aminopyridine-4-carboxylate (MAC) were simultaneously introduced to a blue quasi-2D perovskite film. Relying on the synergistic effect of 2PACz and MAC, it not only modulates the phase distribution inhibiting the n = 2 phase but also greatly improves the electrical property of the quasi-2D perovskite film. As a result, the as-modified blue quasi-2D PeLED demonstrated an external quantum efficiency (EQE) of 17.08% and a luminance of 10142 cd m-2. This study exemplifies the synergistic effect among dual additives and offers a new effective additive strategy modulating phase distribution and building balanced carrier transport, which paves the way for the fabrication of highly efficient blue PeLEDs.

Deciphering the photophysical properties of naphthalimide derivatives using ultrafast spectroscopy.

Naphthalimide derivatives composed of donor-acceptor type structures hold significant promise across a wide range of applications. Here, the solvent polarity and viscosity controlled excited-state dynamics of a naphthalimide derivative with a donor-acceptor structure were studied using multiple spectroscopic techniques. From the stationary spectroscopic investigations, large Stokes shift and low fluorescence quantum yield were observed with increasing the solvent polarity, suggesting a more polar excited state relative to the ground state, which is evidenced by the Lippert-Mataga relationship. We also observe an enhanced fluorescence with a prolonged lifetime in a more viscous solution due to the restriction of excited-state molecular rearrangement. These observations result from the emerged twisted intramolecular charge transfer (TICT) state. The ultrafast spectroscopy studies further unravel a solvent polarity dependent excited state evolution from the intramolecular charge transfer state to the TICT state, revealing that the TICT state can be populated only in strong polar solvents. Control experiments by tuning the solvent viscosity in ultrafast experiments were employed to verify the excited state molecular rearrangement subsequently. These observations collectively emphasize how fine-tuning the photophysical properties of naphthalimide derivatives can be achieved through strategic manipulation of solvent polarity and viscosity.

Efficient and Stable Multicolor Emissions of the Coumarin-Modified Cs3LnCl6 Lead-Free Perovskite Nanocrystals and LED Application.

Lanthanide-based lead-free perovskite materials hold great promise for the development of high-resolution full-color displays in the future. Here, various Cs3LnCl6 perovskite nanocrystals (NCs) emitting light across the visible to near-infrared spectrum with remarkably high photoluminescence quantum yield (PLQY) are systemically prepared. Especially, by introducing multifunctional coumarin small molecules into Cs3EuCl6 NCs as an intermediate state, Cs3EuCl6 NCs can achieve an impressive PLQY of 92.4% with pure red emission and an exceptional energy transfer efficiency of nearly 93.2%. Furthermore, the lanthanide-based electroluminescent devices in red, green, and blue are successfully fabricated. Among them, the Cs3EuCl6-NC-based red light-emitting diode (LED) demonstrates a FWHM of 18 nm at 617 nm, an external quantum efficiency up to 5.17%, and a maximum brightness of 2373 cd m-2, which is the most excellent reported for lead-free narrowband (within 20 nm) emission devices. Notably, these devices exhibit an operating half-life of 440 h at a brightness level of 100 cd m-2, surpassing the performance of most reported lead-free perovskite LEDs (PLEDs). This work opens up exciting possibilities for the future commercialization of lanthanide-based PLEDs in the display industry, paving the way for more vibrant, energy-efficient, and long-lasting display technologies.

Elucidation for the pharmacological effects and mechanism of Shen Bai formula in treating myocardial injury based on energy metabolism and serum metabolomic approaches.

ETHNOPHARMACOLOGICAL RELEVANCE: Shen Bai formula (SBF) is a proven effective traditional Chinese medicine for treating viral myocarditis (VMC) sequelae in clinic, and myocardial injury is the pathological basis of VMC sequelae. However, the pharmacological action and mechanism of SBF have not been systematically elucidated. AIM OF THE STUDY: In present research, the doxorubicin-induced myocardial injury rat model was used to evaluate the efficacy of SBF, and energy metabolism and metabolomics approaches were applied to elucidate the effects of SBF on myocardial injury. MATERIALS AND METHODS: Through energy metabolism measurement system and UPLC-Q-TOF-MS/MS oriented blood metabolomics, directly reflected the therapeutic effect of SBF at a macro level, and identified biomarkers of myocardial injury in microcosmic, revealing its metabolomic mechanism. RESULTS: Results showed that SBF significantly improved the electrocardiogram (ECG), heart rate (HR), extent of myocardial tissue lesion, and ratio of heart and spleen. In addition, the serum levels of AST, CK, LDH, α-HBDH, cTnI, BNP, and MDA decreased, whereas SOD and ATP activity and content increased. Moreover, SBF increased locomotor activity and basic daily metabolism in rats with myocardial injury, restoring their usual level of energy metabolism. A total of 45 potential metabolomic biomarkers were identified. Among them, 44 biomarkers were significantly recalled by SBF, including representative biomarkers arachidonic acid (AA), 12-HETE, prostaglandin J2 (PGJ2), 15-deoxy-Δ-12,14-PGJ2, 15-keto-PGE2, 15(S)-HPETE, 15(S)-HETE, 8,11,14-eicosatrienoic acid and 9(S)-HODE, which involved AA metabolism, biosynthesis of unsaturated fatty acids and linoleic acid metabolism. CONCLUSION: We successfully replicated a myocardial injury rat model with the intraperitoneal injection of doxorubicin, and elucidated the mechanism of SBF in treating myocardial injury. This key mechanism may be achieved by targeting action on COX, Alox, CYP, and 15-PGDH to increase or decrease the level of myocardial injury biomarker, and then emphatically interven in AA metabolism, biosynthesis of unsaturated fatty acids and linoleic acid metabolism, and participate in regulating purine metabolism, sphingolipid metabolism, primary bile acid biosynthesis, and steroid hormone synthesis.

Zinc-Mediated Endoplasmic Reticulum Stress and Metallothionein Alleviate Arsenic-Induced Cardiotoxicity in Cyprinus Carpio.

Arsenic (As) is a natural component of the Earth's crust, and its inorganic form is highly toxic. The problem of As pollution in water is extremely urgent, and its impact on aquatic organisms should be widely considered. Here, 120 common carp were selected as the test subjects and were exposed to environmentally relevant concentrations of As (2.83 mg L- 1) for 30 days. Histomorphological observations showed the adverse effects of As on the heart: irregular arrangement of myocardial fibers, rupture of muscle fiber bundles, inflammatory infiltration, and hemorrhages. Mechanistically, abnormal expression of factors related to As-induced inflammation (TLR4/MYD88/NF-κB pathway), endoplasmic reticulum stress (CHOP, GRP78, ATF6, PERK, IRE1) and oxidative stress (SOD, CAT, Nrf2, HO-1) was observed. Then, we tried to find a protective agent against As-induced myocardial injury. As one of the important metal elements for maintaining cell growth and immunity, zinc (Zn, 1 mg L- 1) significantly alleviated the pathological abnormalities induced by As, and the changes in physiological and biochemical indices in response to As exposure were significantly alleviated by Zn administration, which was accompanied by the restoration of metallothionein (ZIP8, Znt1, Znt5, Znt7) and heat shock protein (HSP60, HSP70, HSP90) expression. These results suggest for the possibilty of developing Zn as a candidate therapeutic agent for As induced aquatic toxicology.

Theoretical Insights into the Dynamics of Gas-Phase Bimolecular Reactions with Submerged Barriers.

Much attention has been paid to the dynamics of both activated gas-phase bimolecular reactions, which feature monotonically increasing integral cross sections and Arrhenius kinetics, and their barrierless capture counterparts, which manifest monotonically decreasing integral cross sections and negative temperature dependence of the rate coefficients. In this Perspective, we focus on the dynamics of gas-phase bimolecular reactions with submerged barriers, which often involve radicals or ions and are prevalent in combustion, atmospheric chemistry, astrochemistry, and plasma chemistry. The temperature dependence of the rate coefficients for such reactions is often non-Arrhenius and complex, and the corresponding dynamics may also be quite different from those with significant barriers or those completely dominated by capture. Recent experimental and theoretical studies of such reactions, particularly at relatively low temperatures or collision energies, have revealed interesting dynamical behaviors, which are discussed here. The new knowledge enriches our understanding of the dynamics of these unusual reactions.

Promoted Charge Separation and Long-Lived Charge-Separated State in Porphyrin-Viologen Dyad Nanoparticles.

Developing light-harvesting systems with efficient photoinduced charge separation and long-lived charge-separated (CS) state is desirable but still challenging. In this study, we designed a zinc porphyrin photosensitizer covalently linked with viologen (ZnP-V) that can be prepared into nanoparticles in aqueous solution. In DMF solution, the monomeric ZnP-V dyads show no electron transfer between the ZnP and viologen units. In contrast, the ZnP-V nanoparticles in aqueous solution show fast charge separation with a CS state lifetime of up to 4.3 ms. This can be attributed to charge hopping induced by aggregation or distance modification between the donor and acceptor induced by electronic interaction. Nevertheless, the lifetime of the CS state is orders of magnitude longer than for molecular aggregates reported previously. The ZnP-V nanoparticles show enhanced photocatalytic hydrogen production as compared to the ZnP nanoparticles and still hold promise for other applications such as photovoltaic devices and photoredox catalysis.

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals.

Accurate determination of the thermal rate constants for combustion reactions is a highly challenging task, both experimentally and theoretically. Machine learning has been proven to be a powerful tool to predict reaction rate constants in recent years. In this work, three supervised machine learning algorithms, including XGB, FNN, and XGB-FNN, are used to develop quantitative structure-property relationship models for the estimation of the rate constants of hydrogen abstraction reactions from alkanes by the free radicals CH3, H, and O. The molecular similarity based on Morgan molecular fingerprints combined with the topological indices are proposed to represent chemical reactions in the machine learning models. Using the newly constructed descriptors, the hybrid XGB-FNN algorithm yields average deviations of 65.4%, 12.1%, and 64.5% on the prediction sets of alkanes + CH3, H, and O, respectively, whose performance is comparable and even superior to the corresponding one using the activation energy as a descriptor. The use of activation energy as a descriptor has previously been shown to significantly improve prediction accuracy ( Fuel 2022, 322, 124150) but typically requires cumbersome ab initio calculations. In addition, the XGB-FNN models could reasonably predict reaction rate constants of hydrogen abstractions from different sites of alkanes and their isomers, indicating a good generalization ability. It is expected that the reaction descriptors proposed in this work can be applied to build machine learning models for other reactions.

Theoretical studies on the kinetics and dynamics of the BeH+ + H2O reaction: comparison with the experiment.

The reaction of BeH+ with background gaseous H2O may play a role in qubit loss for quantum information processing with Be+ as trapped ions, and yet its reaction mechanism has not been well understood until now. In this work, a globally accurate, full-dimensional ground-state potential energy surface (PES) for the BeH+ + H2O reaction was constructed by fitting a total of 170 438 ab initio energy points at the level of RCCSD(T)-F12/aug-cc-pVTZ using the fundamental invariant-neural network method. The total root-mean-square error of the final PES was 0.178 kcal mol-1. For comparison, quasi-classical trajectory calculations were carried out on the PES at an experimental temperature of 150 K. The obtained thermal rate constant and product branching ratio of the BeD+ + H2O reaction agreed quite well with experimental results. In addition, the vibrational state distributions and energy disposals of the products were calculated and rationalized using the sudden vector projection model.

Optimizing glycation control in diabetes: An integrated approach for inhibiting nonenzymatic glycation reactions of biological macromolecules.

Diabetes is a multifactorial disorder that increases mortality and disability due to its complications. A key driver of these complications is nonenzymatic glycation, which generates advanced glycation end-products (AGEs) that impair tissue function. Therefore, effective nonenzymatic glycation prevention and control strategies are urgently needed. This review comprehensively describes the molecular mechanisms and pathological consequences of nonenzymatic glycation in diabetes and outlines various anti-glycation strategies, such as lowering plasma glucose, interfering with the glycation reaction, and degrading early and late glycation products. Diet, exercise, and hypoglycemic medications can reduce the onset of high glucose at the source. Glucose or amino acid analogs such as flavonoids, lysine and aminoguanidine competitively bind to proteins or glucose to block the initial nonenzymatic glycation reaction. In addition, deglycation enzymes such as amadoriase, fructosamine-3-kinase, parkinson's disease protein, glutamine amidotransferase-like class 1 domain-containing 3A and terminal FraB deglycase can eliminate existing nonenzymatic glycation products. These strategies involve nutritional, pharmacological, and enzymatic interventions that target different stages of nonenzymatic glycation. This review also emphasizes the therapeutic potential of anti-glycation drugs for preventing and treating diabetes complications.

Reaction dynamics for the Cl(2P) + XCl → XCl + Cl(2P) (X = H, D, Mu) reaction on a high-fidelity ground state potential energy surface.

In this work, the dynamics of a prototypical heavy-light-heavy abstract reaction, Cl(2P) + HCl → HCl + Cl(2P), is investigated both by constructing a new potential energy surface (PES) and by rate coefficient calculations. Both the permutation invariant polynomial neural network method and the embedded atom neural network (EANN) method, based on ab initio MRCI-F12+Q/AVTZ level points, are used for obtaining globally accurate full-dimensional ground state PES, with the corresponding total root mean square error being only 0.043 and 0.056 kcal/mol, respectively. In addition, this is also the first application of the EANN in a gas-phase bimolecular reaction. The saddle point of this reaction system is confirmed to be nonlinear. In comparison with both the energetics and rate coefficients obtained on both PESs, we find that the EANN is reliable in dynamic calculations. A full-dimensional approximate quantum mechanical method, ring-polymer molecular dynamics with a Cayley propagator, is employed to obtain the thermal rate coefficients and kinetic isotopic effects of the title reaction Cl(2P) + XCl→ XCl + Cl(2P) (H, D, Mu) on both new PESs, and the kinetic isotope effect (KIE) is also obtained. The rate coefficients reproduce the experimental results at high temperatures perfectly but with moderate accuracy at lower temperatures, but the KIE is with high accuracy. The similar kinetic behavior is supported by quantum dynamics using wave packet calculations as well.