Browse Articles

Mechanochemical Synthesis Enables Melting, Glass Formation and Glass–Ceramic Conversion in a Cadmium-Based Zeolitic Imidazolate Framework

Institutional pressures and green supply chain integration intention: Evidence from Chinese manufacturing firms

Green supply chain integration has become the key for manufacturing firms to cope with environmental challenges and gain sustainable competitiveness, but increasing the intention of firms to implement green supply chain integration is still a significant challenge. To respond to this issue, this study aims to theoretically discuss and empirically investigate the influence mechanism of institutional pressures on corporate green supply chain integration intention based on the theory of planned behavior. This study used a survey method to collect data from Chinese manufacturing firms, and the 292 finalized responses were analyzed using SPSS and AMOS. The findings indicate that institutional pressures, i.e., coercive and normative pressures, positively affect firms’ intention to implement green supply chain integration. The study also exposed the executives’ environmental awareness positively moderates the effect of coercive and normative pressures on green supply chain integration intention. Furthermore, executives’ self-efficacy positively moderates the effect of normative pressure on green supply chain integration intention. The findings of this study help deepen the understanding of the formation mechanism of green supply chain integration intention, which provides practical insights for effectively promoting green supply chain integration and realizing green transformation and high-quality development of manufacturing enterprises.

Molecular simulation of hybrid polymer nanocomposites with organic nanodimers and inorganic nanorods: From structure and dynamics to viscosity

Polymer nanocomposites (PNCs) are cutting-edge materials that enhance polymer matrices with nanoparticles to achieve superior performance. The properties of these composites are significantly influenced by interactions at the nanoparticle–polymer interface. This study explores how inorganic nanorods (NRs) and various organic nanodimers (NDs)—differentiated by their interaction with the polymer and including Janus types—impact the structure, dynamics, and viscosity of PNCs. Through molecular simulations, we reveal how these nanoparticles interact within block copolymer and homopolymer matrices. Our findings show that ND–monomer interactions notably affect ND organization and improve barrier properties, while the structuring of NRs contributes to increased mechanical resistance. Furthermore, different PNCs provide a wide range of thickening behavior depending on the polymer matrix and the embedded nanoparticles. We observe increments of up to six times the melt’s viscosity when both nanoparticles are introduced into copolymers. The viscosity of the systems is evaluated using a non-equilibrium method, the SLLOD algorithm, and the Green–Kubo relation to obtain both the shear-thinning curve and the zero-shear viscosity value. These results underscore the importance of nanoparticle interactions and configurations in determining PNC behavior, providing critical insights for advancing material design and functionality.

Photocatalytic and Chemoselective H/D Exchange at α-Thio C(sp³)-H Bonds

Workshop-based participatory research design through the lens of a culture-centered approach (CCA)

Purpose Nurses in the neonatal intensive care unit (NICU) play a crucial role in health communication, yet their voices are often overlooked. This study explores communication challenges from nurses’ perspectives to develop a sustainable communication infrastructure. Methods A workshop-based participatory design (WBPD) was used, where NICU nurses worked collaboratively to identify communication barriers. Thematic analysis was conducted using the culture-centered approach (CCA) to ensure context-specific solutions, integrating both researcher insights and NICU-based communication infrastructure design. Findings A multilevel communication infrastructure model was developed to enhance nurse-parent interactions in the NICU. Key findings highlight that effective communication hinges on three interconnected factors: (1) nurses’ skills and access to resources, (2) institutional policies supporting standardized protocols and mentorship programs, and (3) systemic mechanisms for fostering shared understanding. Participants advocated for structured training, culturally responsive practices, and language support tools to address diverse needs. The proposed model integrates learner-centered training, interprofessional collaboration, communicative algorithms, and healthy boundaries to establish a cohesive, inclusive framework. Conclusions Nurse-led, multilevel interventions are essential for improving NICU communication. The proposed model enhances training, policies, and culturally responsive strategies, supporting more effective nurse-parent interactions and improved neonatal care. Beyond the NICU, this framework offers a transferable model for enhancing communication in other high-stress healthcare environments, ensuring more inclusive and structured communication practices across diverse settings.

Low-energy dissociative recombination of OH+

Dissociative recombination of the OH+ ion with free electrons is modeled theoretically using a recently developed approach that is based on first-principles calculations and multichannel quantum defect theory. The coupling between the incident electron and the rovibrational motion of the ion is accounted for. The cross section of the process at collision energies 10−6–1 eV and the thermally averaged rate coefficient at 10–1000 K are evaluated. The obtained anisotropic rate coefficients agree well with the data from a recent experiment carried out at the Cryogenic Storage Ring, especially when compared to previous theoretical values, which are smaller than the experimental results by about a factor of about 30.

Hexaphenyl-1,2-Diphosphonium Dication [Ph₃P–PPh₃]²⁺: Superacid, Superoxidant, or Super Reagent?

Retraction: Exploring the impact of climate technology, financial inclusion and renewable energy on ecological footprint: Evidence from top polluted economies

Prediction of reaction kinetics for CL-20 and host–guest crystals under high temperature and pressure using neuroevolution potential

The energetic host–guest approach has been successfully applied to design various novel crystal structures. A neuroevolution potential was proposed to predict the reactive kinetics of CL-20 crystals under high temperature and pressure. In this study, molecular dynamics simulations were conducted to investigate the shock compression and thermal decomposition behaviors. During the shock compression process, temperature monitoring revealed the transition of the crystal from the unreacted Hugoniot state to the reacted Hugoniot state, which occurred after the decomposition of CL-20 molecules. The temperature rise in the reacted state followed the order: N2O > CO2 > H2O2 > NCCH3 > β > α > γ > ε. These indicate that guest molecules facilitate the reaction under shock conditions. During the thermal decomposition process, monitoring the potential energy evolution showed that the initial decomposition of CL-20 molecules is an endothermic reaction, primarily producing NO2. As the temperature increased, NO2 was further consumed, and CL-20 underwent a ring-opening reaction, primarily generating CO2. NCCH3 and H2O2 molecules were consumed during the endothermic process, showing the largest and smallest potential energy changes, respectively. N2O molecules were consumed during the formation of final products, while CO2 and H2O were the final products and were not consumed. The activation energy ranking of the reactions was ε > β > γ > NCCH3 > N2O > CO2 > α > H2O2. These results provide an atomic-level perspective for controlling the detonation performance of energetic materials under high temperature and pressure.

Direct Enantioselective Allylic Alkylation of α-Amino Esters to Quaternary Glutamates via Strategic Pyridoxal Catalyst Design

Identification of medicinal plant parts using depth-wise separable convolutional neural network

Identifying relevant plant parts is one of the most significant tasks in the pharmaceutical industry. Correct identification minimizes the risk of mis-identification, which might have unfavorable effects, and it ensures that plants are used medicinally. Traditional methods for plant part identification are often time-consuming and require specific expertise. This study proposed a Depth-wise Separable Convolutional Neural Network (DWS-CNN) to enhance the accuracy of medicinal plant part identification. Furthermore, we incorporated the tuned pre-trained models such as VGG16, Res Net-50, and Inception V3 which are designed by Standard convolutional neural network (S-CNN) for comparative purposes. We trained variants of the Standard convolutional neural network (S-CNN) model with high-resolution images of medicinal plant leaves which contains 15,100 leaf images. The study used supervised learning by which leaf images are used as an identity for the other parts of the plants. We used transfer learning to tune training and model parameters. Experimental results showed that our DWS-CNN model achieved better performance compared to S-CNN models, with an accuracy of 99.84% for training data, 99.44% for F1-score and 99.44% for testing data, which improves in both accuracy and training speed. The presence of depth-wise separable convolution and batch normalization at the fully connected layer of the model made the model achieved a good classification performance.

An elementary derivation of the “|Δμ| big is good” rule and its implications in several reactivity principles

Recently, Miranda-Quintana, Heidar-Zadeh, and Ayers have elaborated a simple proof of the “Δμ big is good” rule for reactions that are dominated by charge transfer, making use of the smooth quadratic interpolation for fractional electron number and the analysis of several derivatives. In this work, we extend this approach, showing that one can derive a direct relationship of the change in the energy as a function of the change in the absolute value of the chemical potential that implies the “Δμ big is good” rule. In addition, we show that this rule plays an important role in the fulfillment of the hard and soft acids and bases (HSAB), the maximum hardness, and the minimum electrophilicity principles, as anticipated intuitively by Parr.

Stepwise Self-Assembly of Multisegment Mesoporous Silica Nanobamboos for Enhanced Thermal Insulation

Retraction: Nexus between energy efficiency, green investment, urbanization and environmental quality: Evidence from MENA region

Single photon emission from point defects in hexagonal boron nitride nanosheets enabled via ambient annealing

Single photon emitters (SPEs) in two-dimensional van der Waals crystals are essential for developing quantum technologies due to their ready integration into photonic circuits and high photon extraction efficiency. Hexagonal boron nitride (h-BN) exhibits an ultra-wide bandgap that can host multiple defect states emitting stable single photons with high brightness at room-temperature. The fabrication and regulation of the defects that determine the spin and optoelectronic physics of h-BN are thus important. Herein, we demonstrate the composite defects modulation in h-BN nanosheets by thermal annealing treatment in air that can generate stable room-temperature SPEs with high photon purity and brightness. Strong and sharp zero-phonon lines appear at ∼386 nm (3.21 eV) and ∼573 nm (2.16 eV) after annealing. The ultraviolet light emission is induced by the formation of a boroxyl ring in h-BN commensurate with the optical transition of nitrogen vacancies, which is characterized by the spectral analysis combined with first-principle calculations. The thermal annealing suppresses the fluorescence background, leading to the population of anti-site nitrogen vacancy complex defects, achieving visible single photon emissions. The results of our work provide a practical post-synthesis process for engineering ensembles of emitters in h-BN for their future integration in quantum photonics.

Nonmetal Organic Frameworks Exhibit High Proton Conductivity

Contribution of silicon fertilizer to soil and growth of Pak choi under reclaimed and brackish water cycling irrigation

Rational utilization and improvement of agricultural water resources has been and is still the focus of research on developing efficient and green agriculture in various countries. Thus, the exploitation and usage of non-traditional water resources hold substantial significance in water resources management and sustainable agriculture. However, their reuse may induce secondary soil salinization and impose stress on crops. To address the challenges of soil salinity and plant stress under brackish-reclaimed water irrigation, this study aimed to investigate the effects of silicon (Si) fertilizer application on soil properties and Pak choi (Brassica rapa L.) performance under two cycling irrigation sequences (RW-BW and RW-RW-BW) and three spraying frequencies (0-, 2-, and 4-day intervals). The findings displayed that the pH of each treatment (7.95-8.10) remained below 8.5, suggesting no risk of secondary soil alkalization. At the same spraying frequency of silicon fertilizer, the soil electrical conductivity (EC) significantly decreased with increasing irrigation times of reclaimed water. Silicon fertilizer improved soil structure and reduced sodium levels, alleviating salinity. The increasing spraying interval of silicon fertilizer provoked the diminution of the SAR and ESP, before rising again. But they were far below the threshold range, and there was no risk of soil salinization (15% and 13 mM1/2). The total silicon content of the soil and leaves increased under the different cycling irrigation conditions. Spraying silicon fertilizer on the crop leaf surface did not significantly influence the total silicon content of the soil. In conclusion, the application of Si-fertilizer beneficially impacts soil physicochemical properties and crop development and mitigates the risk of secondary salinization under brackish-reclaimed water for cycling irrigation.

Precision, intelligence, and a new paradigm for chemical research

Chemists have long struggled to precisely regulate and create substances, often relying on trial-and-error methods that are inefficient for complex, high-dimensional research challenges. However, recent advancements in computational and experimental techniques, particularly those with artificial intelligence (AI), are providing new avenues for precision and intelligent chemistry. This perspective highlights the synergistic integration of accurate theoretical simulations, advanced experimental characterization, and AI-driven models, creating a closed-loop system to accelerate chemical discovery and material design. At the core of this framework is an iterative process: precise computational and experimental data lead to advanced intelligent models, which guide the design of optimized reaction parameters or chemical components, and direct robotic platforms that perform reproducible, high-throughput experiments. These experimental data, in turn, provide continuous feedback to refine intelligent models, ultimately enabling precise control of reaction conditions and material properties. To fully realize this vision, we advocate the development of key infrastructures: a multidisciplinary, multimodal, and standardized AI-ready chemical database as a data foundation; a knowledge and logic-enhanced large chemical model for intelligent prediction and design; distributed, full-process robotic laboratories for automated experimentation; and a cloud platform for resource sharing and collaboration. Together, these components constitute a vision for robotic chemist cloud facilities, which will empower researchers with unparalleled capabilities to seamlessly integrate precision and intelligence. This integrated approach promises to accelerate discovery and represents a paradigm shift in chemical research.

Creation of Artificial Subcellular Organelles Using Compartmentalized <i>Escherichia coli</i> Bodies for Artificial Metalloenzyme-Mediated Abiotic Catalysis in Eukaryotic Cells

Characterizing fermentable carbohydrate foods in the diets of children with abdominal pain-related disorders of gut-brain interaction and healthy children

Objectives Restricting dietary fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) can alleviate symptoms in children with disorders of gut-brain interaction (DGBI). Due to the restrictions of a low FODMAP diet (LFD), a less restrictive FODMAP Gentle diet (FGD) has been suggested. However, the types and amounts of high FODMAP foods and carbohydrates commonly consumed by children have not been studied. We aimed to identify the high FODMAP foods and proportions of FODMAP carbohydrates consumed by children with DGBI and healthy children (HC) and to determine which usually ingested FODMAPs would be restricted on the FGD. Methods Three-day diet records from both children with DGBI and HC were analyzed and compared to assess the type and amount of high FODMAP foods and carbohydrates ingested. Additionally, the ingested FODMAPs that would be restricted on the FGD were determined. Results Diet records from 77 children with DGBI and 64 HC were analyzed. The number of foods ingested daily was similar between children with DGBI and HC (12.3 ± 4.2 vs 12.9 ± 3.4, respectively); high FODMAP foods comprised most foods eaten in both groups. Children with DGBI (vs. HC) ate fewer high FODMAP foods per day (6.5 ± 2.3 vs 8.7 ± 2.4, P &lt; 0.0001, respectively). Fructans were the most consumed FODMAP carbohydrate in both groups, and children with DGBI (vs. HC) consumed fewer fructans, lactose, fructose, and polyols (all P &lt; 0.0001). The top 3 food categories consumed in both groups were wheat-containing foods, dairy, and fruits/ 100% fruit juices. In children with DGBI, 80.9% of the high FODMAP foods consumed would be limited on the FGD. Conclusions Children with DGBI consume fewer high FODMAP foods and carbohydrates than HC, with the top consumed FODMAP carbohydrates being fructans, lactose, and fructose. A FGD would restrict most high FODMAP foods consumed by children with DGBI.