Reasonable Mechanism Research Articles

Fabrication of Catechol Sensitized Hollow Microspheres ZnTiO3/TiO2/ZnO for the Photodegradation of Methylene Blue

AbstractDesigning new materials that can be used to treat environmental pollutants is one of the prime focuses of chemical science. Here, an unprecedented photocatalytic material with high activity of catechol sensitized ZnTiO3/TiO2/ZnO hollow microspheres was synthesized by hydrothermal method. The structure and properties of the composite photocatalyst were analyzed by various testing methods. The photocatalytic activity was evaluated by using methylene blue (MB) as the target degradation substance. The results demonstrated that catechol (CA) sensitized ZnTiO3/TiO2/ZnO photocatalyst is a hollow microsphere with a diameter of 1–1.5 mm, the CA molecules are covalently bonded to the ZnTiO3/TiO2/ZnO surface, and the composite photocatalyst has good light absorption and carrier separation performance. Under the condition of irradiation for 90 min, the degradation rate of the composite photocatalyst for MB was higher than that of ZnTiO3/TiO2/ZnO. When the loading amount of CA was 0.6 wt%, the degradation rate of CA/ZnTiO3/TiO2/ZnO for MB was as high as 98.28 %, and the reaction rate constant was up to 4.35×10−2 min−1. A reasonable mechanism of photocatalytic degradation of pollutants by catechol as sensitizer on ZnTiO3/TiO2/ZnO was proposed. This study provides a theoretical reference for efficient photodegradation of pollutants in the environment.

Mechanistic insights into the influence of preparation methods and Fe3+ doping on the low-temperature performance of MnCeOx catalyst for NH3-SCR reaction

Aiming to polish up the denitrification efficiency and N2 selectivity for MnCeOx catalyst at low temperature, an array of Fe3+-doped MnCeOx catalysts were synthesized through innovative approaches: the citric acid method (FeMnCeOx-CA) and CTAB-assisted template method (FeMnCeOx-ST). However, the catalysts prepared by CTAB-assisted template method (MnCeOx-ST and FeMnCeOx-ST catalysts) did not exhibit an excellent low-temperature activity compared with the catalysts made by citric acid method (MnCeOx-CA and FeMnCeOx-CA catalysts) below 300 °C. Therefore, an in-depth investigation of underlying impact of citric acid method and Fe3+ doping on MnCeOx catalyst was thoroughly implemented. We finally found that the main function of citric acid is to enhance the internal electron transfer of the catalyst by facilitating the mutual conversion between active components, particularly Mn4+/Mn3+, thereby improving the denitrification activity of FeMnCeOx-CA catalyst at low temperature. Further, the NH3-SCR performance of FeMnCeOx-CA exceed 90 % NOx conversion within 75–150 °C, and it can be reached 94 % NOx conversion at 125 °C, which was almost 10 % higher than that of MnCeOx-CA catalyst. Moreover, the N2O formation of FeMnCeOx-CA is significantly suppressed at temperatures ranging from 125-200 °C, exhibiting a higher N2 selectivity compared to MnCeOx-CA. Besides, solid solution structure of −Ce-O-Mn-O-Fe- formed after Fe3+ doping in MnCeOx catalyst strengthened surface acidity and redox ability over FeMnCeOx-CA catalyst, promoting NH3 adsorption and activation. On the other hand, the FeMnCeOx-CA catalyst exhibits the highest content of Ce3+ and Mn4+, facilitating oxygen vacancies formation and redox capacity, thereby promoting the key intermediates generation in the microscopic NH3-SCR reaction process. Finally, a reasonable Langmuir-Hinshelwood mechanism and detailed reaction pathways of NH3-SCR reaction existed on FeMnCeOx-CA catalyst were also proposed.

Phosphoric Acid Catalyzed N-Addition/ C-Addition Reaction of 3-Vinyl Indoles with Pyrazole/Pyrazolone to Construct Pyrazole-Substituted 3-(1-Heteroarylethyl)-indole Scaffolds.

Developing a highly efficient atom-economic method for the preparation of 3-(1-heteroarylethyl)-indole scaffolds is of significant value in pharmaceutical and agricultural chemistry. Herein, a phosphoric acid-catalyzed N-addition reaction of 3-vinyl indoles with pyrazoles and C-addition reaction of 3-vinyl indoles with pyrazolones were developed. A series of pyrazole-substituted 3-(1-heteroarylethyl)-indole scaffolds were synthesized in excellent yields (up to 99% yield) under mild reaction conditions. A reasonable reaction mechanism was proposed to explain the experimental results.

Synergistic effect of Li, La co-doping on photocatalytic activity of BaTiO3 ferroelectric material for effective degradation of toxic NOx for environmental remediation

We investigated the synergistic effects of Li, La-doping and co-doping on the A-site of perovskite BaTiO3 microstructure as a catalyst for the removal of toxic NOx for environmental remediation. The Rietveld refinement of XRD and Raman analysis confirmed that at room temperature, the ceramic samples possessed a pure-tetragonal perovskite structure, with P4mm space group. SEM images revealed a denser microstructure with reduced grain size on Li, La doping in BaTiO3. XPS results confirmed the coexistence of Li and La as doping elements into BaTiO3 lattice. EPR and PL spectra demonstrate the presence of Ti and Ba vacancies as well as defects. According to the DRS results, the absorption edge of the co-doped sample was blue-shifted. In frequency-dependent dielectrics, the co-doped sample exhibits the greatest rise in dielectric permittivity. Impedance measurement demonstrates the fastest rate of charge transport in Ba0.98Li0.01La0.01TiO3. Similarly, Li and La incorporation into BaTiO3 significantly reduced the recombination extent of charge carriers as evidenced by the PL. Under UV light, the photocatalytic NOx degradation with Ba0.98Li0.01La0.01TiO3 achieved a remarkable 44 % efficiency during 60 min. This enhancement is associated with improved charge carrier separation at the interface between the ferroelectric surfaces. Only a marginal decrease in photocatalytic activity occurred after four consecutive runs. A reasonable photocatalytic mechanism for the degradation of NOx over Ba0.98Li0.01La0.01TiO3 is proposed. This study's findings can inspire the design and synthesis of rare earth and alkali metal ion co-doped photocatalysts with built-in electric fields of a ferroelectric material for efficient NOx removal from the environment.

Built-in electric field intensified by SPR effect to drive charge separation over Ag-AgI/Sb2S3 heterojunction for high-efficiency photocatalytic metronidazole mineralization

The photocatalytic activity of semiconductor is extremely limited due to the rapid recombination of photo-generated carriers and low utilization of solar energy. Herein, a novel Ag-AgI/Sb2S3 (A-S) heterojunction with superior photocatalytic activity was synthesized, in which the optimal Ag-AgI/Sb2S3 heterojunction exhibited 31 times photocatalytic activity of metronidazole removal than that of than that of Sb2S3. The excellent performance of the prepared photocatalysts could be attributed to the synergy of surface plasmon resonance effect by in situ-generated Ag nanoparticles and the built-in electric field of heterostructure, which would broaden the response range of solar light and accelerate the transfer of photogenerated carriers. Besides, characterization and theoretical calculations confirm Ag atoms acquire abundant electrons from Sb atoms of Sb2S3, allowing Ag+ to reduce to Ag0. Meanwhile, a portion of the Ag0 plasma-induced hot electrons reduce O2 to ·O2–, while the remaining portion flows to AgI and circulates in AgI and Ag0 to form a closed loop, thus eliminating the photo-corrosion of AgI. The stability of A-S photocatalyst was also confirmed by five cycle-tests. Furthermore, a reasonable photocatalytic mechanism was proposed by analyzing the intermediates of metronidazole. This work has implications for designing efficient photocatalysts using high-performance materials based on Ag nanoparticles.

Cu(I)-Photosensitizer-Catalyzed Olefin-α-Amino Radical Metathesis/Demethylenative Cyclization of 1,7-Enynes.

A demethylenative En-Yne radical cyclization of 1,7-enynes has been successfully developed to chemoselectively afford 3,4-dihyroquinolin-2-ones or quinolin-2-ones under the catalysis of Cu(I) photosensitizers PS3 and PS6 with different redox potentials. The preliminary mechanistic experiments revealed that the reaction underwent an unprecedented olefin-α-amino radical metathesis-type process. A reasonable mechanism was proposed to illustrate the catalyst-controlled chemoselectivity of the reaction based on preliminary mechanistic experiments and DFT calculations.

Overview of knowledge reasoning for knowledge graph

Knowledge graphs are large-scale semantic networks that considerably impact knowledge representation. Mining hidden knowledge from existing data, including triplet knowledge reasoning, is a primary objective of knowledge graphs. With the development of Neural Network (NN) and Deep Learning (DL), the interpretability of triplet knowledge reasoning gradually decreases; furthermore, what machines learn is not actual reasoning but digital reasoning shortcuts. To solve this problem, more background knowledge needs to be introduced into knowledge graphs: causal graphs can offer valuable causal logic knowledge for reasoning; temporal quadruples can provide essential temporal distribution details; and commonsense graphs can furnish pertinent commonsense understanding to support reasoning. In recent years, many scholars have incorporated additional background knowledge into knowledge graphs to construct more complex reasoning mechanisms. This paper reviews the basic concepts and definitions of knowledge reasoning and the reasoning methods used for knowledge graphs. Specifically, we dissect the reasoning methods into four categories: triplet reasoning, causal inference, temporal inference, and commonsense reasoning. Finally, we discuss the remaining challenges and research opportunities related to knowledge graph reasoning.

Artificial Intelligence and Scientific Research:Prospects and Risks———Synthesis of the session “Artificial Intelligence and Paradigm Change in Science and Technology Innovation” in Tianjin Forum 2023

In the sub-forum “Transformation of Artificial Intelligence and Paradigm in Science and Technology Innovation” of Tianjin Forum 2023, scholars from both China and abroad discussed the impact of artificial intelligence. With the deepening development of the new technological revolution and industrial transformation, new-generation artificial intelligence technology is continuously making breakthroughs in research and application. AI not only promotes the transformation of material productivity, but also gradually emerges as a critical engine for enhancing the knowledge productivity. Since the emergence of the concept of "AI for Science", it has become an obvious proposition generally accepted by the academic community for its tremendous enabling capabilities for knowledge production. Artificial intelligence technology continues to achieve breakthroughs and gain widespread infiltration into the scientific research field, introducing new elements and momentum into scientific research and significantly catalyzing the enhancement of scientific research efficiency and paradigm shifts. AI-driven scientific research has become a new frontier in the global application of artificial intelligence. However, as artificial intelligence triggers paradigm shifts in social science research, the issues of data security, ethics, and value alignment that it brings about need to draw attention from the social science community. Scholars participating in the forum generally concurred that, in regulating the enabling role of AI in scientific research, it is imperative to take into account the specificities of various disciplines and stages, and comprehensively reasonable rational risk allocation mechanisms, platform. support mechanisms, and collaborative participation frameworks to achieve prudent, agile, and full lifecycle regulation of AI for Science.

Local environmental fiscal expenditures, industrial structure upgrading, and carbon emission intensity

To realize the development goals of environmental governance and low-carbon emission reduction, the effect of local environmental protection fiscal expenditure on carbon emissions needs to be assessed. This paper utilizes the panel data of 30 provinces in mainland China except Tibet from 2007 to 2019 and empirically examines the baseline effect, the mechanism, the nonlinear effect, and the spatial spillover effect of the local environmental protection fiscal expenditures affecting the intensity of carbon emissions by using the fixed-effects model, the FGLS model, the threshold panel model, and the spatial Durbin model. The results indicate that local environmental fiscal expenditure significantly lowers carbon emissions, generating a carbon reduction effect, and this finding remains robust under various checks; the mechanism analysis reveals that local environmental fiscal expenditure facilitates industrial structure upgrading, thereby indirectly producing a carbon reduction effect; the threshold analysis shows that the carbon reduction effect of local environmental fiscal expenditure exhibits a diminishing marginal tendency, and as the industrial structure continues to upgrade, the carbon reduction effect of local environmental fiscal expenditure displays an “Inverted U-shaped” pattern; the spatial analysis demonstrates that local environmental fiscal expenditure and carbon emissions have positive spatial autocorrelation, and local environmental fiscal expenditure in one region will induce a decline in carbon emissions in neighboring regions through spatial spillover effect. Based on the above results, this paper puts forward policy suggestions in terms of constructing a reasonable growth mechanism for local environmental protection financial expenditures, creating an ecological environment for low-carbon transformation, and constructing a reasonable regional coordination mechanism for carbon emission reduction.

Cross-modality feature fusion for night pedestrian detection

Night pedestrian detection with visible image only suffers from the dilemma of high miss rate due to poor illumination conditions. Cross-modality fusion can ameliorate this dilemma by providing complementary information to each other through infrared and visible images. In this paper, we propose a cross-modal fusion framework based on YOLOv5, which is aimed at addressing the challenges of night pedestrian detection under low-light conditions. The framework employs a dual-stream architecture that processes visible images and infrared images separately. Through the Cross-Modal Feature Rectification Module (CMFRM), visible and infrared features are finely tuned on a granular level, leveraging their spatial correlations to focus on complementary information and substantially reduce uncertainty and noise from different modalities. Additionally, we have introduced a two-stage Feature Fusion Module (FFM), with the first stage introducing a cross-attention mechanism for cross-modal global reasoning, and the second stage using a mixed channel embedding to produce enhanced feature outputs. Moreover, our method involves multi-dimensional interaction, not only correcting feature maps in terms of channel and spatial dimensions but also applying cross-attention at the sequence processing level, which is critical for the effective generalization of cross-modal feature combinations. In summary, our research significantly enhances the accuracy and robustness of nighttime pedestrian detection, offering new perspectives and technical pathways for visual information processing in low-light environments.

A synthetic agent to simulate decisional behaviour of designers working with an active recommender framework system

Application-specific reasoning mechanisms (ASRMs) development is a rapidly growing domain of systems engineering. A demonstrative implementation of an active recommender system (ARS) was realized to support designing ASRMs and to circumvent procedural obstacles by providing context-sensitive recommendations. The specific problem for the research presented in this paper was the development of a synthetic validation agent (SVA) to simulate the decisional behaviour of designers and to generate data about the usefulness of the recommendations. The fact of the matter is that the need for the SVA was raised by the pandemic, which prevented involving groups of human designers in the recommendation testing process. The reported research had three practical goals: (i) development of the logical fundamentals for the SVA, (ii) computational implementation of the SVA, and (iii) application of the SVA in data generation for the evaluation of usefulness of recommendation. The SVA is based on a probabilistic decisional model that quantifies decisional options according to the assumed decisional tendencies. The three key concepts underlying the SVA are (i) decisional logic, (ii) decisional knowledge, and (iii) decisional probability. These together enable generation of reliable data about the decisional behaviours of human designers concerning the obtained recommendations. The completed tests proved the above assumption.

Pricing ancillary service of a Francis hydroelectric generating system to promote renewable energy integration in a clean energy base: Tariff compensation of deep peak regulation

The active Deep Peak Regulation (DPR) of a Francis Hydroelectric Generating System (FHGS) is crucial to large-scale consumption of renewable energy in clean energy bases. The traditional vertical integrated dispatching method of FHGS lacks a reasonable compensation mechanism, leading to its negativity behaviours in DPR. To improve its DPR's enthusiasm, a quantitative method for the opportunity cost of DPR is derived by revealing the interaction relationship between the DPR benchmark, opportunity cost and efficiency loss. The tariff compensation strategy for DPR is proposed based on the interaction relationship. We then applied this compensation strategy to hourly dispatching model of a clean energy base. The results show that this strategy effectively balances the contradiction between DPR's enthusiasm and the curtailment rate. For example, when the tariff compensation is 0.20 RMB/kWh and the DPR's benchmark coefficient is higher than 67%, the tariff compensation strategy achieves the increased profits for FHGS, WFs, and PVs. Therefore, when the owners of power sources in the clean energy base are different, the tariff compensation strategy proposed in this study could be used to ensure the profits of power sources and improve the power quality provided by the clean energy base.

Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification

Multimodal remote sensing data classification can enhance a model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks has become the focus of researchers. Inspired by the human feedback learning mechanism, causal reasoning mechanism, and knowledge induction mechanism, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposes causal meta-reinforcement learning (CMRL). First, based on the feedback learning mechanism, we overcame the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we helped the agents understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information.Second, based on the causal inference mechanism, we designed causal distribution prediction actions, classification rewards, and causal intervention rewards, capturing pure causal factors in multimodal data and preventing false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets showed that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

Challenging Task of Ni-Catalyzed Highly Regio-/Enantioselective Semihydrogenation of Racemic Tetrasubstituted Allenes via a Kinetic Resolution Process.

The first earth-abundant transition metal Ni-catalyzed highly regio- and enantioselective semihydrogenation of racemic tetrasubstituted allenes via a kinetic resolution process as a challenging task was well established. This protocol furnishes expedient access to a diversity of structurally important enantioenriched tetrasubstituted allenes and chiral allylic molecules with high regio-, enantio-, and Z/E-selectivity. Remarkably, this semihydrogenation proceeded with one carbon-carbon double bond of allenes, which was regioselective complementary to the Rh-catalyzed asymmetric version. Deuterium labeling experiments and density functional theory (DFT) calculations were carried out to reveal the reasonable reaction mechanism and explain the regio-/stereoselectivity.

Thiocyanate facilitating thiosulfate extraction of gold via inhibiting formation of passive layer

Thiosulfate leaching is a feasible alternative for cyanidation. However, thiosulfate is easily decomposed and forms the passive layer on the ore surface, thus hindering gold extraction. In this paper, by adding thiocyanate to reduce the undesirable effect, copper-ammonia-thiosulfate leaching system is utilized to extract gold from the calcine. This research investigated the factors affecting gold extraction and kinetics of the leaching system, and the potential reason and reaction mechanism of the thiocyanate for improving gold leaching efficiency were discussed and analyzed. Gold leaching percentage is increased from 62.99% to 88.45% by adding thiocyanate that is close to that of cyanidation. By using X-ray photoelectron spectroscopy, gold in the leachate may be in the form of gold(I) thiosulfate complex. The leaching mechanism and the reason for improving gold extraction by adding thiocyanate were investigated via characterization and the leaching thermodynamic analysis. The possible reason for improving gold leaching percentage lied in that the thiocyanate reduced the decomposition of thiosulfate, thus inhibiting the form of passivation layer. According to the leaching mechanism, thiocyanate is used as an additive in the copper-ammonia-thiosulfate leaching system, which is of great significance for facilitating gold recovery, lessening thiosulfate consumption, and reducing ammonia dosage.

Non-metal inducing charge rearrangement in carbon nitride to promote photocatalytic hydrogen production

Photocatalytic hydrogen production technology offers a means of converting solar energy into chemical energy contained in hydrogen for human consumption. However, traditional photocatalysts restrict the progress of photocatalytic technology owing to the straightforward complexation of carriers and lack of active sites. Thus, in this work, the number of active sites and carrier separation efficiency have been significantly improved by non-metallic modification and modulation of the geometry of carbon nitride. It has been demonstrated that oxygen doping enhances the energy band structure of benzene-substituted O-doped g-CN nanotubes (BOCN). Oxygen, in conjunction with the benzene ring, creates redox energy level positions that are spatially separated. One-dimensional tubular structures synthesised by supramolecular self-assembly have a thin-walled structure capable of exposing more active sites. Additionally, the adsorption equilibrium of H+ on the catalyst is further enhanced. The in-depth analysis of each component through experiments and theoretical calculations contributes to a reasonable photocatalytic mechanism for decomposing aquatic hydrogen.

Bispidine‐Based S,N‐Chiral Ligands for Palladium‐Catalyzed Asymmetric Arylation of Cyclic N‐Sulfonyl Ketimines

AbstractDevelopment of new chiral ligands is essentially important in the field of asymmetric metal catalysis. Bispidines, steming from natural alkaloids, represent an intriguing yet underexplored class of chiral ligands. In this article, we developed a series of bispidine‐based chiral ligands with chirality locked in the side chain, and explore their properties by applying in the palladium‐catalyzed asymmetric addition of arylboric acids to cyclic N‐sulfonyl ketimines. A S,N‐bispidine ligand was found especially efficient, affording a series of chiral benzosultams with a quaternary chiral center in excellent yields with excellent ee values. Density functional theory (DFT) calculations and crystal structure of catalyst helped to propose a reasonable mechanism including a possible transition state model to explain the origin of stereoselectivity.

Model-based impact evaluation of new tuberculosis vaccines in aging populations under different modeling scenarios: the case of China.

The slow descent in TB burden, the COVID-19 pandemic, along with the rise of multidrug-resistant strains of Mycobacterium tuberculosis, seriously threaten TB control and the goals of the End TB strategy. To fight back, several vaccine candidates are under development, with some of them undergoing the phases 2B and 3 of the development pipeline. The impact of these vaccines on the general population needs to be addressed using disease-transmission models, and, in a country like China, which last year ranked third in number of cases worldwide, and where the population is aging at a fast pace, the impact of TB vaccination campaigns may depend heavily upon the age of targeted populations, the mechanistic descriptions of the TB vaccines and the coupling between TB dynamics and demographic evolution. In this work, we studied the potential impact of a new TB vaccine in China targeting adolescents (15-19 y.o.) or older adults (60-64 y.o.), according to varying vaccine descriptions that represent reasonable mechanisms of action leading to prevention of disease, or prevention of recurrence, each of them targetting specific routes to TB disease. To measure the influence of the description of the coupling between transmission dynamics and aging in TB transmission models, we explored two different approaches to compute the evolution of the contact matrices, which relate to the spreading among different age strata. Our findings highlight the dependence of model-based impact estimates on vaccine profiles and the chosen modeling approach for describing the evolution of contact matrices. Our results also show, in line with previous modeling works, that older adult vaccination is a suitable option in China to reduce the incidence of TB as long as the vaccine is able to protect already exposed individuals. This study underscores the importance of considering vaccine characteristics and demographic dynamics in shaping TB control strategies. In this sense, older adult vaccination emerges as a promising avenue for mitigating TB transmission in China but also remarks the need for tailored intervention strategies aligned with demographic trends.

Analysis of the Tripartite Evolutionary Game between MRV Institutions, Government, and Enterprises in the Context of the Carbon Trading Market

As the global carbon emission trading market develops, the role of Monitoring, Reporting, and Verification (MRV) institutions is becoming increasingly important. This paper analyzes the tripartite evolutionary game among MRV institutions, local government, and enterprises within the carbon trading market context and explores the impact of various factors on market behavior through sensitivity analysis. We find that changes in carbon prices, market maturity, the rent-seeking cost of enterprises, penalty policies of local government, and the supervisory intensity of MRV institutions significantly influence the strategic choices of the participating entities. Notably, high carbon prices may lead to collusive behaviors between enterprises and local government, while strengthened supervision and a reasonable penalty mechanism can promote fairness and justice in the market.

NiCoP cocatalyst modified g-C3N4 as ohmic junction photocatalyst for efficient degradation of tetracycline under visible light

Increasing the electron-hole recombination rate in g-C3N4 can effectively improve its photocatalytic performance. In this work, NiCoP/g-C3N4 (NCP/PCN) composites with ohmic junction were formed by embedding granular NiCoP in irregularly porous g-C3N4. There was almost no barrier between the metal and the semiconductor in ohmic junction, which made it easier for electrons to slip from PCN to NCP along the curved energy band, and NCP acted as an electron collector to rapidly capture the slipping electrons. In addition, porous g-C3N4 prepared by supramolecular self-assembly could provide a shorter diffusion path for electrons. Thus, the electron-hole was effectively separated and the photocatalytic performance was improved. The band electronic structure and existence of ohmic junction in 7-NCP/PCN composite were demonstrated by XPS, ESR and DFT calculation. Finally, a reasonable photocatalytic degradation mechanism and possible tetracycline degradation path were proposed. This work has significant potential for providing an effective method for the design of non-precious metal photocatalysts.