Summary The electrochemical synthesis of chemicals and fuel feedstocks has been demonstrated to be a sustainable and “green” alternative to traditional chemical engineering, where oxygen evolution reaction (OER) plays a vital role in coupling with various cathodic reactions. While tremendous attention, involving both research and review topics, has been focused on pushing the limit of OER catalysts’ activity, the long-term stability of OER catalysts, which may play an even more important role in large-scale electrolysis industrialization, has been much less emphasized. Until this point, few systematic strategies for developing OER catalysts with industrially relevant durability have been reported. In this review, critical mechanisms that could influence OER stability are summarized, including surface reconstruction, lattice oxygen evolution, and the dissolution-redeposition process of catalysts. Moreover, to bridge the gap between lab-scale OER tests and large-scale electrocatalysis applications, stability considerations in electrolyzer design for long-term operation are also discussed in detail. This review provides catalyst and reactor design principles for overcoming OER stability challenges and will focus more attention from the field on the great importance of OER stability as well as future large-scale electrocatalysis applications.
Invented by Alessandro Volta and Félix Savary in the early 19th century, circuits consisting of resistor, inductor and capacitor (RLC) components are omnipresent in modern technology. The behavior of an RLC circuit is governed by its circuit Laplacian, which is analogous to the Hamiltonian describing the energetics of a physical system. Here we show that topological insulating and semimetallic states can be realized in a periodic RLC circuit. Topological boundary resonances (TBRs) appear in the impedance read-out of a topolectrical circuit, providing a robust signal for the presence of topological admittance bands. For experimental illustration, we build the Su-Schrieffer–Heeger circuit, where our impedance measurement detects the TBR midgap state. Topolectrical circuits establish a bridge between electrical engineering and topological states of matter, where the accessibility, scalability, and operability of electronics synergizes with the intricate boundary properties of topological phases.The discovery of topological insulators has given rise to a flourishing field dedicated to the investigation of the topological state of matter. This manuscript contributes to this field by introducing the idea of a topoelectrical circuit, whereby an assembly of conventional circuit elements realises various topological band structures.
Flow chemistry and continuous processing can offer many ways to make synthesis a more sustainable practice. These technologies help bridge the large gap between academic and industrial settings by often providing a more reproducible, scalable, safe and efficient option for performing chemical reactions. In this review, we use selected examples to demonstrate how continuous methods of synthesis can be greener than batch synthesis on a small and a large scale.
ABSTRACT Synthetic bioelectronics is rapidly advancing, propelled by breakthroughs in synthetic biology and bioelectronics. This convergence is key to next‐generation wearable and implantable devices, enabling seamless integration with living systems. Here, we introduce an enzymatic hydrogel electrode (GelZymes) developed via a synthetic bioelectronic strategy to overcome the mechanical and interfacial limitations of conventional enzyme electrodes. GelZymes deliver two core advances: i) a monolithic and scalable 3D architecture that unifies the enzyme membrane and electrode, simplifying fabrication and eliminating interfacial instability; and ii) tissue‐like viscoelasticity—combining stretchability and adhesiveness—rarely achievable with rigid enzyme membranes. GelZymes are synthesized through three steps: engineering a stretchable, mixed‐conducting 3D hydrogel; implementing an enzyme‐compatible, cascading crosslinking scheme to immobilize enzymes within the network; and balancing the trade‐off between electronic/ionic conductivity and the density of redox‐active enzyme sites to maximize bio‐electrochemical performance. We further show that GelZymes enable a shift from invasive, tissue‐interfaced biosensing to noninvasive, tissue‐integrated biosensing, offering a practical pathway to bridge current biosensor technologies with living systems.
In this study, the synergistic effects of Fe doping and oxygen vacancies on the structural, electronic, and optical properties of Bi<sub>4</sub>O<sub>5</sub>Br<sub>2</sub>, as well as their influence on the photocatalytic CO<sub>2</sub> reduction mechanism, were systematically explored through first-principles calculations. The results reveal that Fe-doped, oxygen-defective, and Fe–Vo co-modified Bi<sub>4</sub>O<sub>5</sub>Br<sub>2</sub> systems exhibit excellent thermodynamic and dynamic stability. Oxygen vacancies introduce defect states near the Fermi level, narrowing the band gap and enhancing charge localization and CO<sub>2</sub> adsorption, while Fe doping induces strong spin polarization and introduces Fe <i>3d</i> impurity levels that effectively couple with O <i>2p</i> orbitals, promoting charge transfer and visible-light absorption. The coexistence of Fe dopants and oxygen vacancies produces a significant synergistic effect, forming a continuous energy-level bridge that enhances charge separation and broadens the light absorption range. Gibbs free energy analyses further demonstrate that the Fe–Vo–BOB system exhibits the lowest energy barriers and the most favorable thermodynamics for CO<sub>2</sub>-to-CO conversion. This study provides deep insight into the defect–dopant synergy in Bi<sub>4</sub>O<sub>5</sub>Br<sub>2</sub> and offers valuable theoretical guidance for engineering highly efficient visible-light-driven photocatalysts in solar energy conversion and environmental remediation.
Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a critical factor in system performance and behavior. Despite their growing role in SE research, prompt-related decisions are rarely documented in a systematic or transparent manner, hindering reproducibility and comparability across studies. To address this gap, we conducted a two-phase empirical study. First, we analyzed nearly 300 papers published at the top-3 SE conferences since 2022 to assess how prompt design, testing, and optimization are currently reported. Second, we surveyed 105 program committee members from these conferences to capture their expectations for prompt reporting in LLM-driven research. Based on the findings, we derived a structured guideline that distinguishes essential, desirable, and exceptional reporting elements. Our results reveal significant misalignment between current practices and reviewer expectations, particularly regarding version disclosure, prompt justification, and threats to validity. We present our guideline as a step toward improving transparency, reproducibility, and methodological rigor in LLM-based SE research.
Alexandra González, Xavier Franch, Silverio Martínez-Fernández
Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.
Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara
Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.
The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.
Accurate deformation prediction is crucial for ensuring the safety and longevity of bridges. However, the complex fluctuations of deformation pose a challenge to achieving this goal. To improve the prediction accuracy, a bridge deformation prediction method based on a bidirectional gated recurrent unit (BiGRU) neural network and error correction is proposed. Firstly, the BiGRU model is employed to predict deformation data, which aims to enhance the modeling capability of the GRU network for time-series data through its bidirectional structure. Then, to extract the valuable information concealed in the error, a transformer model is introduced to rectify the error sequence. Finally, the preliminary and error prediction results are integrated to yield high-precision deformation prediction results. Two deformation datasets collected from an actual bridge health monitoring system are utilized as examples to verify the effectiveness of the proposed method. The results show that the proposed method outperforms the comparison model in terms of prediction accuracy, robustness, and generalization ability, with the predicted deformation results being closer to the actual results. Notably, the error-corrected model exhibits significantly improved evaluation metrics compared to the single model. The research findings herein offer a scientific foundation for bridges’ early safety warning and health monitoring. Additionally, they hold significant relevance for developing time-series prediction models based on deep learning.
The safety, longevity, and healthy operation and maintenance of world-class large bridges are a research hotspot that continues to attract attention from academia and industry. In particular, during the sustainable operation period of large and statically indeterminate bridges under various loads and complex environmental conditions, it is necessary to establish an information-based intelligent structural health monitoring and early warning cloud platform system to ensure the safety and economic efficiency of in-service bridges. Through interdisciplinary research in computer science, communication engineering, automation control, and engineering mechanics, this article established a multi-factor complex modal multi-source theoretical model and applied the real-time early warning of bridge monitoring data and the coupling of finite element models to verify the robustness of the intelligent cloud model under the influence of multiple factors on statically indeterminate bridges. This work solves the technical barriers that traditional technical monitoring cannot achieve continuous real-time, spatiotemporal and remote monitoring of statically indeterminate structures, and realizes an intelligent cloud platform model for spatial, direct and automated monitoring, providing scientific and technological guarantees for the healthy maintenance of super-large bridges, and providing theoretical scientific support and paradigms for saving labour and reducing maintenance costs.
The construction of bridge pile foundations in karst regions faces significant risks, including karst collapse and ground subsidence caused by dewatering during excavation. Both karst collapse and shallow soil cavity subsidence are influenced by numerous factors, which significantly complicates the risk assessment of bridge pile foundation construction in these areas. This study proposes a risk assessment method for bridge pile foundation construction in karst regions based on the Fuzzy Analytic Hierarchy Process (FAHP). An evaluation index system for pile foundation construction risks was established through experimental research, expert surveys, and data analysis. Additionally, the concept of fuzzy mathematics was introduced to quantify the weights of the indicators, enabling the comprehensive assessment of multi-source risks. Experimental results from bridges across the project area demonstrate that this method exhibits a certain level of reliability in assessing the risks of bridge pile foundation construction in karst regions, with the evaluation results aligning well with actual conditions.
In response to the significant autogenous shrinkage issue of ultra-high performance concrete (UHPC) under normal temperature curing, the influence of lightweight aggregates with water-holding capacity mixed with coarse aggregates on the compressive strength and autogenous shrinkage performance of UHPC was investigated. The mixing proportions of the two types of aggregates were optimized using the response surface method (RSM). The results show that adequate amounts of basalt and volcanic rocks with water-holding capacity effectively reduce the shrinkage of UHPC. The rigid basalt restrains the shrinkage of the matrix, while volcanic rocks with water-holding capacity release moisture slowly, alleviating the autogenous drying effect and thus reducing shrinkage. However, an excessive amount of basalt and volcanic rocks leads to a decrease in the compressive strength of UHPC. This is because the coarse aggregates increase the transition zone between the matrix and coarse aggregates, resulting in fiber clustering, and the low strength of volcanic rocks makes it difficult to compensate for the degradation effect brought about by the transition zone.
In tunnel construction, the quantitative evaluation of rock mass integrity heavily relies on information from the exposed face, and there are challenges when drilling data is used for integrity evaluation. To this end, this study introduced a novel method for quantitative evaluation of rock mass integrity during drilling, integrating numerical statistics with machine learning. A substantial dataset of digital drilling data was collected, covering three common types of rock mass integrity: relatively intact, relatively fractured, and fractured. Subsequently, a high-performance random forest model for the classification of rock mass integrity was developed through data preprocessing and hyperparameter optimization. The interpretability of the model’s predictive results was enhanced using Shapley additive explanations (SHAP) value theory. Additionally, the instability index and its exponents from the multivariable instability index analysis method were selected and quantified, and the quantitative evaluation model for “interval rock fracture index (IIRFI)” was created. The application of the model in actual tunnel engineering demonstrates an approximate 90% accuracy rate in evaluating the three types of rock mass integrity. The model provides more efficient, accurate, and detailed information on rock mass integrity compared to conventional methods. The study offers a new and effective quantitative approach for assessing rock mass integrity in tunnels, which contributes to improved construction safety and project efficiency.
Abstract Effective heat transfer between the working fluid and subterranean rocks is essential for producing green and low-carbon geothermal energy. As the primary thermal conductive medium, cement has low thermal conductivity, leading to high thermal resistance and significantly reducing geothermal wells’ efficiency. Therefore, high thermal conductivity cement has emerged as a widely anticipated new research area. The purpose of this research is to address the substantial harm of traditional carbon-based thermal conductive fillers to cement. A novel expanded graphite (EG)/epoxy resin (EP) composite additive (MEG) was designed to increase cement’s thermal conductivity while preserving its mechanical strength and pumpability. Firstly, the physicochemical properties of MEG were revealed by FT-IR, UV-Vis, SEM, and TGA. Then, the applicability of MEG cement in adverse geological environments (high-temperature 60–100 ℃, high-mineralization 5–36% NaCl) was evaluated through simulated maintenance experiments. Finally, the hydration products and pore structure of MEG-cement were analyzed by XRD/FT-IR and SEM/MIP, revealing the thermal conductivity enhancing mechanism. The results showed that: 1) MEG uses ZDMA as a bridge to promote the ring opening and curing of EP, and is formed by strong cation -π interaction with EG. 2)After curing at 60–100 ℃, MEG-cement exhibits a significant increase (46.6-182.1%) in thermal conductivity within the optimal dosage range of 5–10%, fully meeting the requirements for compressive strength (10.4–21.7 MPa) and fluidity (19.3–21.2 cm) of cementing. In addition, MEG-cement maintained stable density and significant high thermal conductivity advantage in high-mineralization environments (5–36% NaCl), with an increase in thermal conductivity of 23.8- 54.1%. 3) The mechanism of MEG promoting heat transfer in cement is summarized as the enhancement of the hydration process and the production of C-S-H gels. C-S-H gels filled the gel pores and transition pores in the cement skeleton and formed a dense, high thermal conductivity network, which shortens the heat transfer path and thus greatly improves the thermal conductivity of cement. In summary, this study has successfully developed a MEG geothermal cement with independent intellectual property rights that provides reliable technical support for the efficient development of geothermal resources and has important engineering application value.
In order to ensure the smooth construction and long-term stable operation of silty clay sections in the Zhoukou–Pingdingshan highway project, this paper conducted one-dimensional creep tests on silty clay samples with different compaction degrees under different stress levels and studied the effect law of compaction degree on its long-term creep mechanical properties. By establishing the corresponding creep constitutive models, a theoretical basis was provided for the prevention and control of creep failure in silty clay subgrades in engineering practice. Research results show that: ① Under the same stress level, with the increase of compaction degree, the instantaneous strain, creep strain, and total strain of the samples all decrease significantly. Among them, the compaction degree has the most obvious inhibitory effect on the creep strain of the samples, followed by the total strain, and the inhibitory effect on the instantaneous strain of the samples is minimal; ② The variation laws of the void ratio‑logarithm of time (e-lg t) curves of samples with different compaction degrees are basically consistent, which can all be divided into three stages. At the same stress level, the total reduction in void ratio of the samples becomes smaller as the compaction degree increases; ③ Under the same stress level, with the increase of compaction degree, the parameters E1, E2, η1, and η2 in the Burgers model all show an increasing trend, indicating that the instantaneous elastic deformation, viscoelastic deformation, and the steady-state creep rate of the samples are all decreasing, while the duration of the samples in the stable-state creep stage is increasing.