Introduction Recently, the integration of deep learning techniques and computational materials science has catalyzed significant advances in the microstructural analysis of materials, particularly through the lens of multiscale, high-dimensional imaging data. However, conventional models often fall short in capturing the intricate topology and spatial variability that define realistic microstructural patterns, limiting their ability to inform material property predictions, inverse design, and structural synthesis. Methods To overcome these challenges, we introduce an innovative deep learning framework designed for microstructural image classification and representation learning, incorporating physical, geometric, and topological constraints directly into the training process. Our method, centered on the structured generative model MorphoTensor, introduces hierarchical tensorial embeddings that retain directionality, anisotropy, and spatial locality—features crucial for realistic material modeling. We further incorporate a Topology-Aware Latent Refinement strategy, which couples persistent homology with differentiable approximations of Betti numbers to enforce topological consistency and augment microstructural diversity. Unlike existing data-driven pipelines, our framework seamlessly integrates statistical encoding, topologicalization, and latent manifold alignment within a unified architecture, ensuring robustness across diverse datasets including phase-field simulations and real microscopy data. Results and Discussion Empirical evaluations on benchmark and experimental datasets demonstrate that our method significantly outperforms standard convolutional and autoencoding baselines in accuracy, stability, and generalization. Moreover, our approach aligns closely with the ongoing efforts in the broader computational materials and mechanics communities to build interpretable, physically informed, and adaptable deep learning systems. These contributions illustrate the potential of structured deep generative modeling as a foundational tool for advancing intelligent microstructure analysis and design in materials informatics.

The growing integration of AI into educational and professional settings raises urgent questions about how human creativity evolves when intelligent systems guide, constrain, or accelerate the design process. Generative AI offers structured suggestions and rapid access to ideas, but its role in adopting genuine innovation remains contested. This paper investigates the dynamics of human-AI collaboration in challenge-based design experiments, applying established creativity metrics: fluency, flexibility, originality, and elaboration in order to evaluate outcomes and implications in an engineering education context. Through an exploratory quasi-experimental study, a comparison of AI-assisted and human-only teams was conducted across four dimensions of creative performance: quantity, variety, uniqueness, and quality of design solutions. Findings point to a layered outcome: although AI accelerated idea generation, it also encouraged premature convergence, narrowed exploration, and compromised functional refinement. Human-only teams engaged in more iterative experimentation and produced designs of higher functional quality and greater ideational diversity. Participants’ self-perceptions of creativity remained stable across both conditions, highlighting the risk of cognitive offloading, where reliance on AI may reduce genuine creative engagement while masking deficits through inflated confidence. Importantly, cognitive offloading is not directly measured in this study; rather, it is introduced here as a theoretically grounded interpretive explanation that helps contextualize the observed disconnect between performance outcomes and self-perceived creativity. These results bring opportunities and risks. On the one hand, AI can support ideation and broaden access to concepts; on the other, overreliance risks weakening iterative learning and the development of durable creative capacities. The ethical implications are significant, raising questions about accountability and educational integrity when outcomes emerge from human-AI co-creation. The study argues for process-aware and ethically grounded frameworks that balance augmentation with human agency, supporting exploration without eroding the foundations of creative problem-solving. The study consolidates empirical findings with conceptual analysis, advancing the discussion on when and how AI should guide the creative process and providing insights for the broader debate on the future of human–AI collaboration.

A series of substituted indole derivatives have been synthesized and evaluated for their atypical antipsychotic activity Compared to traditional neuroleptics, second-generation or "atypical" antipsychotics offer a more favorable therapeutic profile against both positive and negative symptoms of schizophrenia. The compounds were designed based on their physicochemical similarity studies to standard drugs and in silico (docking studies) with 5-HT2A and D2 receptors. The prepared compounds were evaluated for atypical antipsychotic activity in animal models of dopaminergic (apomorphine-induced mesh climbing behavior and stereotypy) and serotonergic antagonism (1-(2,5-dimethoxy-4-iodophenyl)-2 aminopropane (DOI) induced head twitch assay). All the test compounds showed. The potential of these compounds to penetrate the blood-brain barrier (log BB) was computed through an online software program, and the values obtained for the compounds suggest good potential for brain permeation. In-silico (docking studies) suggested good binding of the test compounds to the 5-HT2A and D2 receptors and a hypothetical binding model for the target compounds was postulated. The prepared test compounds, designated as 8 to 15, exhibited an atypical antipsychotic profile in the pharmacological assays with a mechanistic profile of combined 5-HT2A and D2 antagonism. The study has afforded novel indole-based lead molecules with potential atypical antipsychotic effect.

High-valent 3d-metal incorporated layered double hydroxides (LDHs) have emerged as a novel class of abundant and highly effective water splitting electrocatalysts. These materials demonstrate adjustable electronic architectures, a large number of active sites, and exceptional surface area, rendering them extremely efficient for facilitating the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Recent advances have highlighted that high-valent 3d-metal (Mn3+/Mn4+, Cr3+/Cr4+, V3+/V4+) incorporation in LDHs enhances catalytic activity, accelerates reaction kinetics, improves charge transfer, and increases stability. While numerous reviews have covered LDHs as promising electrocatalysts for water splitting, most have primarily focused on general synthetic strategies, compositional tuning, and morphological optimization of LDHs containing 3d transition metals such as Ni, Co, Fe, Zn, and Cu. However, a critical and comprehensive analysis emphasizing the progress and advancement of high-valent 3d-metal-incorporated LDHs for water splitting remains absent. In this review, we comprehensively examine the design principles, synthesis strategies, and mechanistic insights underpinning the efficacy of high-valent 3d-metal-incorporated LDHs in water splitting. Particular attention is given to strategies including heteroatom doping, interfacial engineering, defect engineering, electronic tuning, and electrochemical activation. We also discuss state-of-the-art in situ/operando spectroscopic techniques that have deepened the understanding of active phases and dynamic surface reconstruction during catalysis. Finally, we discuss the existing obstacles and future possibilities for the rational design of next-generation LDH catalysts with improved stability, activity, and scalability for sustainable hydrogen production.

Facile synthesis of a near-infrared fluorophore based on Dibenzo[def,mno]chrysene-6,12-dione acceptor for dual applications in organic light-emitting diode and cellular imaging.

The management of cancer relies crucially on early diagnosis and personalized treatment. Real-time analysis of tumor markers within the tumor microenvironment via liquid biopsy opens potential pathways for effective cancer treatment and improved survival rates. Detecting low-abundance tumor markers in bodily fluids, particularly during early-stage cancer, poses significant challenges for traditional methods. Electrochemical sensors have emerged as the preferred technology for liquid biopsy. The exceptional multifunctionality of covalent organic frameworks (COFs)-novel crystalline porous organic polymer materials-has led to significant attention in electrochemical sensing; these features include tunable topologies, controllable pore sizes, and strong π-π stacking interactions. Recent advances in COF-based electrochemical sensors for liquid biopsy are summarized here, with details on COF design principles, synthesis and functionalization methods, and electrochemical reaction mechanisms. The focus is on the use of COFs as novel functional materials in electrochemical sensors for detecting tumor markers. Enhancement strategies for COF-based electrochemical sensors are also explored. An in-depth discussion on translating COF-based electrochemical sensors from laboratory achievements into clinical applications is also presented, covering the associated opportunities, challenges, and future research directions. The aim of this review is to offer concise yet profound guidance on the clinical translation of COF-based electrochemical analytical methods, which can contribute to advancing human health and precision diagnostics.

Reasonable design and efficient synthesis of multifunctional electrocatalysts are crucial for promoting energy conversion, green manufacturing, and pollutant resourcing.

The disconnect between AI-generated molecules with desirable properties and their synthetic feasibility remains a critical bottleneck in computational discovery of drugs and materials. While generative AI has accelerated the proposal of candidate molecules, many of these structures prove challenging or impossible to synthesize using established chemical reactions. Here, we introduce SynTwins, a novel retrosynthesis-guided molecule design framework that finds synthetically accessible molecular analogs by emulating expert chemists' strategies in three steps: retrosynthesis, searching similar building blocks, and virtual synthesis. Using a search algorithm instead of a stochastic data-driven generator, SynTwins outperforms state-of-the-art machine learning models at exploring synthetically accessible analogs while maintaining high structural similarity to original target molecules. Furthermore, when integrated into existing molecular property-optimization frameworks, our hybrid approach produces synthetically feasible analogs with minimal loss in property scores. Our comprehensive benchmarking across diverse molecular datasets demonstrates that SynTwins effectively bridges the gap between computational design and experimental synthesis, providing a practical solution for accelerating the discovery of synthesizable molecules with desired properties for a wide range of applications.

Ensuring broad‐spectrum visible‐light absorption and efficient electron extraction is essential for enhancing the efficiency of photocatalytic hydrogen production. To achieve this, manipulating carrier dynamics through cocatalyst heterojunction engineering has attracted considerable concern. However, conventional narrow‐bandgap Cu–In–Zn–S (CIZS) nanocrystals (NCs) typically exhibit limited photocatalytic activity due to severe exciton annihilation. Herein, two‐dimensional (2D) CIZS nanobelts (NBs) were coupled with Ni 9 S 8 cocatalyst to construct a library of CIZS/Ni 9 S 8 Schottky heterojunctions synthesized via a combined colloidal one‐pot and hot‐injection strategy. As anticipated, the CIZS/2.0%Ni 9 S 8 heterojunction displayed the optimal photocatalytic hydrogen evolution activity of 2.75 mmol g −1 h −1 , ≈3.31 times higher than that of pristine CIZS NBs (0.83 mmol g −1 h −1 ). Experimental results uncovered that the enhanced photocatalytic performance originated from the formation of the CIZS/2.0%Ni 9 S 8 Schottky heterojunction, which facilitated efficient charge transfer from CIZS NBs to Ni 9 S 8 and hindered the return of electrons. Moreover, Ni 9 S 8 serves as active catalytic sites, significantly accelerating surface proton reduction reactions. This study provides valuable insights into the rational design and precise synthesis of colloidal multinary Cu‐based chalcogenide heterojunctions for efficient photocatalytic energy conversion.

Harnessing solar energy via semiconductor-based photocatalysis offers a sustainable solution for global energy and environmental challenges. Therefore, the development of high-performance photocatalysts is a crucial strategy for mitigating the energy crisis and environmental pollution. Further, photocatalytic hydrogen production (H2) from water splitting is the most promising clean technology for renewable energy conversion. Towards this, exploring magnetic materials and their nanocomposites has gathered substantial attention for green H2 generation. This review summarizes advances in ferrite-based photocatalysts, including hematite, spinel ferrites, and magnetite, as well as their nanocomposites with carbon materials, metal oxides (MOS), conducting polymer, metal–organic frameworks (MOFs), and Maxene. Different synthesis strategies and structural modifications are discussed, highlighting their roles in enhancing charge separation, light absorption, and improving catalytic properties. Particular emphasis is given to the correlation between magnetic properties and photocatalytic performance, as well as the recyclability of these materials. Current challenges, including stability, scalability, and limited photocatalytic efficiency, are critically examined. Finally, future perspectives are presented, focusing on rational material design, multifunctional heterostructures, and scalable synthesis methods for efficient and durable hydrogen production.

The traditional purpose of synthesis aims at timing closure, but in modern design flows, tools often require multiple iterative adjustments to achieve optimal results due to the complexity of designs with multimillions of placeable objects. Logic synthesis, the process of generating optimized logic-level representations from high-level descriptions, plays a crucial role in high-performance microprocessors and microcontrollers design methodologies, especially with the rapid advances in integrated circuit technology and increasing design complexity, with a focus on achieving timing convergence. This paper discusses the challenges and improvements (methodologies) associated with synthesis and placement in the context of modern chip design. The experimental results presented in this paper suggest an approach that creates an efficient design flow, eliminating placement and synthesis iterations, leading to timing improvements. This paper discusses the different approaches that have been used to achieve an operating frequency of 1.2Ghz with minimal power & area for a CPU Sub-system. Logical Synthesis has been carried out using Synopsys Design Compiler (DC) while the PnR implementation tool is Cadence Innovus. Using different strategies and automations, the complete synthesis to placement cycle has been reduced leading to a more robust flow for best PPA. This approach includes Register cloning, Data path optimization, Pipelining and Placement aware synthesis approach

Despite being frequently proposed as a low-carbon solution for wastewater treatment and solar fuel production, the feasibility of photocatalytic processes in large-scale deployments remains unclear. This review evaluates the scalability of photocatalytic technologies by synthesizing a decade (2015–2025) of techno-economic analysis (TEA) and life-cycle assessment (LCA) studies. Using a systematic search and programmatic screening, 77 assessment-focused publications were identified from an initial corpus of 854 studies. Across applications, TEA and LCA consistently highlight two dominant barriers to scale-up: high electricity demand in UV-driven systems and significant cradle-to-gate impacts associated with catalyst synthesis, particularly for nanostructured materials. When solar irradiation replaces artificial light, environmental and economic hotspots shift from energy use to material production, catalyst durability, and reuse assumptions. Wide variability in reported costs and impacts reflects heterogeneous methodologies, limited pilot-scale data, and a lack of standardized reporting. Overall, assessment-based evidence indicates that photocatalysis is not yet ready for widespread industrial deployment as a large industrial process. However, continuous advances in solar-driven reactor design, low-impact and circular catalyst synthesis, hybrid process integration, and harmonized TEA/LCA frameworks could substantially improve its prospects for scalable, climate-positive implementation, especially in the context of emerging green energy alternatives.

Purpose Computational design synthesis (CDS) provides a systematic means to explore the design space of complex systems. However, the scope of exploration in many CDS studies is biased by limited parametrization, where component parameters remain fixed or arbitrarily assigned. This paper investigates the influence of configuration and parametric diversity on the effectiveness of design-space exploration in vehicle powertrain synthesis. Design/methodology/approach A simulation-driven CDS framework is developed with an autonomous control tuning mechanism integrated to ensure consistent evaluation of the synthesized topologies. Powertrain topologies are synthesized by randomly interconnecting port-compatible components and assigning parameters from predefined ranges. Two topology sets are generated, one emphasizing configuration diversity and another emphasizing parametric diversity, to analyze their impact on exploration. Findings The results indicate that parameter initialization strongly influences perceived performance. A topology’s parameter assignment defines its effective position in design space, and poor initialization can bias evaluation outcomes even with structural diversity. Research limitations/implications The framework is limited to sequential topologies, which makes it easier to explore the entire design space but also makes the results harder to generalize. Practical implications The groundwork established here impacts the development of generative topology synthesis frameworks, enabling autonomous generation, control tuning and simulation of systems. Originality/value The novelty is the autonomous control tuning integrated in a CDS workflow, alongside the investigation into the influence of configuration and parametric diversity on the effectiveness of design-space exploration.

Bismuth (Bi)‐based materials have emerged as highly promising catalysts for electrochemical CO 2 reduction (CO 2 RR) toward formate production, owing to their high intrinsic catalytic activity and low cost. However, a critical challenge for practical application is the dynamic electrochemical reconstruction of Bi‐based catalysts during CO 2 RR electrocatalysis, an inherently uncontrollable and indeterminate process that often leads to severe structural degradation of catalysts and a subsequent decay in long‐term stability and selectivity. Herein, this review focuses on the development of stabilization strategies to moderate the dynamic reconstruction of Bi‐based catalysts. In detail, this review systematically analyzes and summarizes three distinct principal strategies: (1) passive suppression strategy, which preserves high‐valent Bi δ+ species by suppressing reconstruction; (2) proactive direction strategy, which directs a controllable “self‐optimization” reconstruction toward desired active species; and (3) circumvention strategy, which circumvents the complex reconstruction process through direct synthesis and modification of metallic Bi catalysts. This review aims to construct a rigorous theoretical framework for the rational design and controllable synthesis of advanced Bi‐based catalysts, thereby outlining some potential directions for the future industrial applications.

The enantioselective synthesis of P-stereogenic compounds has emerged as a central focus in modern asymmetric catalysis, driven by their pivotal roles as ligands (Ls), organocatalysts, and bioactive molecules. Over the past decade, significant advances have been made in developing catalytic strategies that enable precise control over phosphorus stereochemistry, expanding both the structural diversity and synthetic utility of these scaffolds. This review highlights recent progress in two key areas: direct P-C bond formation and desymmetrization. P-C bond-forming approaches include cross-coupling reactions of secondary phosphines or their oxides with aryl, alkyl, or benzyl halides, as well as hydrophosphination of alkenes and alkynes. Desymmetrization strategies encompass nucleophilic substitution at P(V) centers, cyclization, C-H activation (CHA), phenolic -OH activation, and P-O alkylation/arylation. Mechanistic insights into these transformations have been discussed, along with the derivatization of P-chiral products and their applications in catalysis, L design, and bioactive molecule synthesis. This comprehensive overview shall serve as a valuable resource for researchers working in asymmetric organophosphorus chemistry.

Macrocyclic arenes are a class of host molecules featuring precisely adjustable cavities and abundant recognition sites, which facilitate rich host-guest interactions. Their bridging structures-such as methylene groups, conjugated units, and heteroatoms (e.g., O, S, N, and Si)-can be rationally designed to fine-tune cavity microenvironments, conformational dynamics, and optoelectronic properties. These capabilities make them key building blocks for constructing molecular containers, sensors, and smart supramolecular assemblies. In recent years, macrocyclic arenes constructed from various aromatic building units and bridging groups have gained widespread attention due to their highly symmetrical rigid frameworks, tunable electronic properties, and rich host-guest chemical behaviors. This article systematically reviews the research progress in this class of macrocyclic arenes, focusing on the regulatory effects of different bridging structures on their cavity sizes, conformational features, and optoelectronic properties. It summarizes the latest developments in synthetic strategies such as one-pot methods, fragment coupling, and post-synthetic modifications and discusses their applications in molecular recognition, pollutant adsorption, optoelectronic material construction, and stimuli-responsive systems. Finally, we look ahead to the challenges and development prospects facing macrocyclic arenes in design synthesis and future applications, aiming to provide useful references and insights for research in this field.

Identification of hPIF1 helicase inhibitors by virtual screening of a Fsp3-enriched library.

Bioinspired hydroxyapatite nanoparticle: Functionalization with chondroitin sulfate-thiol/amine moieties for synergistic bone tissue regeneration and oxidative stress mitigation.

Design and facile synthesis of CoFe nanoparticles functionalized with boron and nitrogen co-doped ultra-thin mesoporous carbon nanosheets: The advanced glucose and hydrogen peroxide electrochemical sensors

Design and experimental synthesis of VOx/SSZ-13 catalyst based on first principles calculations - elucidating the role of VOx microstructure and valence state in propane dehydrogenation