Journals
2026 EN
Yuan Xiaoxi · Yan Chaohui · Zhang Shaochen
+4 more
Abstract Flash Joule‐heating (FJH) technology emerges as a transformative advancement in electrothermal processing with minimal carbon footprint, which leverages high‐intensity electrical pulses to rapidly heat conductive materials to extreme temperatures (often exceeding 3000 K), inducing dramatic structural transformations through non‐equilibrium pathways, thus displaying feasibility across various application scenarios. Noteworthily, FJH technology has evolved from a laboratory practice to a cornerstone of sustainable materials engineering, witnessing ever‐growing researching interests. Thus, a holistic and timely summary of the recent progress and development of FJH technology is of crucial importance to deepen the understandings and update the remaining difficulties in each application that need to be resolved. While challenges still remain on inhibiting the scaled production upon the deployment of FJH, as such, this review proposes perspectives and strategies to help address key unresolved challenges ghosting FJH technology, in order to make the quick landing of FJH‐led industrial revolution and production.
Journals
2026 EN
Zhou Xin · Wang Yani · Zhang Dongrui
+10 more
Abstract Ammonia, as a strategic hydrogen derivative, enables intercontinental energy trade. However, marine applications lack thermal integration studies under spatial and intermittent constraints. Herein, we propose a novel Heat Recovery Multi‐Stage Seawater Vacuum Distillation for Electrolytic Water and Ammonia Generation Process (HR‐SDEA) that synergistically combines waste heat recovery with multi‐stage seawater desalination and ammonia synthesis. This advanced configuration reduces external steam reliance by 40% through repurposing electrolyzer waste heat compared to Seawater vacuum Distillation for Electrolytic water and Ammonia generation process (SDEA), with a modular design suiting offshore environments. Compared with SDEA, the non‐renewable energy demand (NED) decreases by 9.56% and greenhouse gas (GHG) emissions reduce by 11.58% per ton of ammonia. Particularly, this configuration reduces carbon emissions by 87.68% and energy consumption by 83.89% over traditional coal‐to‐ammonia process (CTA) technologies. Combining thermal optimization and modularity, this work breaks bottlenecks for green H 2 ‐NH 3 chains, enabling viable offshore ammonia.
Journals
2026 EN
Chen Yuxin · Zhang Qian · Zeng Xuefu
+1 more
Abstract This study focuses on designing molecular switches to regulate the activity of glutamate dehydrogenase from Thermus thermophilus (3AOG) by altering its intramolecular electric field. By molecular dynamics simulations and interaction network analyses, critical residues involved in the proton‐coupled electron transfer pathway were identified, revealing their roles in interdomain and interfacial electron transfer within the enzyme. Key residues with significant remote electrostatic effects on the electric field at the proton donor (K111) and the catalytic site (D151) were defined. Residues contributing more than 1 MV cm −1 to the electric field at the active site were identified as primary influencers of enzyme activity. Two molecular switches were designed: positive switch (3AOG‐6NZX‐PM) and negative switch (3AOG‐6NZX‐NM). Experimental validation showed that the positive molecular switch enhanced enzymatic activity by 2.11‐fold compared to the negative mutant. This study bridges computational design and biocatalytic applications, highlighting the potential of molecular electro‐switches for functional regulation of enzyme.
Journals
2026 EN
Liao Xudong · Gong Xiangyang · Yang Lei
+2 more
Abstract Liquid bridging cylinders are ubiquitous in nature and industrial processes. The morphology and capillary force of these bridges between cylinders are influenced by several factors, including inter‐particle spacing, cylinder diameter, liquid volume and wettability. This study combines experimental and Surface Evolver (SE) simulations to systematically investigate the effects of these factors on bridge morphology and capillary force. The results indicate that capillary bridge force decreases as particle spacing increases. Conversely, increasing liquid volume enhances capillary bridge force and induces a reverse morphological transition. For cylinder diameters between 1 and 6 mm, the capillary force increases with increasing particle size. Based on experimental and numerical results, we propose a nonlinear regression model for accurate prediction of capillary force. It exhibits greater generality in predicting capillary bridge forces compared to the Princen model. These findings offer valuable theoretical insights for controlling liquid bridges in relevant engineering and industrial applications.
Journals
2026 EN
Wang Bo · Li Tan · Xie Xuhao
+3 more
Abstract The efficient removal of hydrogen sulfide (H 2 S) is crucial for clean energy and environmental protection, with amine‐based absorption being the mainstream due to its cost‐effectiveness and high efficiency. This work designed eight hybrid absorbents using organic diamines and ethylene glycol (EG). Absorption capacity measurements revealed that at 303.15 K and 1 bar, short‐chain diamines exhibited ~1 mol/mol, while long‐chain ones reached ~2 mol/mol. Notably, the 1,4‐butanediamine‐EG reached a remarkable capacity of 1.7 mol/mol at 0.2 bar, and the N , N , N ′, N ′‐tetramethyl‐1,6‐hexanediamine‐EG reached 2.06 mol/mol at 1 bar. Absorption capacity positively correlated with diamine chain length, and primary/secondary amines outperformed tertiary amines at low partial pressures. A reaction equilibrium thermodynamic model was established to verify the structure–capacity relationship via parameters like equilibrium constant K . The selectivity of H 2 S/CO 2 was studied. This provides theoretical guidance for H 2 S absorbent design and industrial applications.
Journals
2026 EN
Deng Chang · Yu Zhendong · Ju Xueyan
+7 more
Abstract High‐entropy oxides (HEOs) are promising heterogeneous catalysts due to their multiple active sites and structural stability, but their application is limited by complex synthesis and nanoparticle sintering. Here, we present a defect‐induced strategy to construct strong metal‐support interactions (SMSI) between MnCeNiCuCo HEO nanoparticles and defect‐rich hexagonal boron nitride nanosheets (h‐BNNS), forming HEO/h‐BNNS. Contrary to classical H 2 ‐induced SMSI, the inherent N/B vacancies in h‐BNNS anchor the HEO and induce spontaneous B‐atom migration over the HEO surface under N 2 , forming a permeable B–O encapsulation. This encapsulation not only inhibits sintering but also induces electronic coupling with the HEO lattice, modulating local charge density and generating abundant oxygen vacancies. Using aerobic oxidative desulfurization as a model reaction, HEO/h‐BNNS achieves a 99.9% desulfurization efficiency. This work demonstrates a defect‐driven pathway to engineer supported high‐entropy catalysts and provides a rational framework for designing efficient, durable, and scalable catalytic systems for energy and environmental applications.
Journals
2026 EN
Magara Tinashe · Phahlane Mampilo
ABSTRACT Artificial Intelligence (AI) is playing an increasingly vital role in the Industrial Internet of Things (IIoT), enabling predictive analytics, real‐time monitoring, and autonomous operations across industries such as manufacturing, logistics, and energy. However, widespread adoption is hindered by technological, organizational, and infrastructural challenges. This paper examines the adoption, application, and challenges of AI–IIoT environments, focusing on implementation domains, adoption drivers, enabling technologies, and key barriers. We conducted a Systematic Literature Review (SLR using PRISMA). Peer‐reviewed English‐language journal articles published between 2018 and 2025 were sourced from ScienceDirect, Web of Science (WoS), Scopus, IEEE Xplore, Springer, Google Scholar, Elsevier, and Taylor & Francis. After applying inclusion criteria and screening procedures, 46 relevant journal articles were included for analysis. Key AI applications identified include predictive maintenance, anomaly detection, real‐time monitoring, autonomous process control, and smart supply chains. Adoption is facilitated by external enablers 5G infrastructure, regulatory support, and internal factors, organizational readiness, and workforce skills. Challenges include data quality issues, cybersecurity risks, legacy system integration, and limited model scalability. Technologies such as edge computing, cloud platforms, and federated learning are instrumental in mitigating these challenges. While adoption is growing, significant barriers remain. AI has the potential to drive operational efficiency and innovation in IIoT, provided these constraints are addressed. This paper offers a comprehensive taxonomy of AI applications and proposes a framework of adoption factors, offering valuable insights for researchers, practitioners, and policymakers involved in AI‐driven industrial transformation.
Journals
2026 EN
Vavekanand Raja · Turab Muhammad · Kumar Teerath
ABSTRACT Generative adversarial networks (GANs) have shown remarkable potential in medical image synthesis but face persistent challenges in achieving diagnostic‐grade quality, particularly in preserving anatomical edges and avoiding mode collapse. To address these limitations, we present a stable edge‐aware GAN architecture featuring two key innovations: dynamically learnable bilateral kernels that adaptively enhance structural gradients during training, and a layered generator with interpolated skip connections to maintain spatial coherence. Our methodology leverages adversarial training with Wasserstein regularization on the CT Kidney Dataset (12,446 images), optimizing for both global fidelity and local precision. Comprehensive experiments demonstrate the model's superiority through quantitative metrics—achieving a 43% improvement in Fréchet Inception Distance (FID = 87 vs. DCGAN's 149, p < 0.01), 16% higher edge sharpness (Sobel gradient magnitude 45.2 ± 3.1 vs. 38.9 ± 4.2), and 0.82 ± 0.05 SSIM scores. Clinical validation by board‐certified radiologists confirmed 89% diagnostic plausibility for synthetic images, with particular praise for tumor boundary delineation. The architecture also shows exceptional training stability, reducing loss fluctuations by 34% compared to conventional GANs while efficiently scaling to 128 × 128 resolution. These results establish a new benchmark for privacy‐preserving medical data augmentation, offering immediate value for scenarios with limited annotated datasets. Future directions include extension to 3D volumetric synthesis and integration with diffusion models for multi‐modal applications, potentially revolutionizing how healthcare institutions generate and share synthetic patient data without compromising privacy.
Journals
2026 EN
Kumari Pratima · Kadian Sachin · Vora Mukund
+4 more
In today's era of enormous text data, topic modeling is emerging as a revolutionary tool in natural language processing. From the corpus of scientific research articles to social media posts and newspaper headlines, topic modeling is employed in several domains to discover the latent primary themes associated with the corpus. This article provides an extensive and comprehensive review of different topic modeling techniques from their origin to the present. The effectiveness and efficacy of different topic modeling techniques, such as non‐negative matrix factorization, latent Dirichlet allocation, latent semantic analysis, probabilistic latent semantic analysis, Top2Vec, and BERTopic, are reviewed to highlight their strengths and weaknesses. A concise summary of recent studies in healthcare, bioinformatics, scientific research articles, social media platforms, and legal domains is also presented. Different quantitative and qualitative evaluation metrics are also discussed to understand the performance of topic modeling techniques better. Finally, a brief discussion on existing challenges and prospects of topic modeling is also included, providing researchers with insight into further advancements in topic modeling.
Journals
2026 EN
Bolhasani Ehsan · Aboutalebi Seyed Hamed · Merrikhi Yaser
Multisensory integration (MSI) is a core brain function underlying perception, learning, and behavior. Understanding the computational mechanisms of MSI is key to advancing AI and brain‐inspired systems. While earlier models relied on probabilistic frameworks, recurrent neural networks (RNNs) offer advantages in capturing temporal dynamics and neural computations. This review presents a critical examination of computational models of MSI, focusing on the evolution from probabilistic integration to modern RNN‐based methods. Biological evidence for temporal coordination in multisensory areas is analyzed and explored how different RNN architectures (e.g., vanilla, long short‐term memory, and gated recurrent unit) simulate these dynamics. Comparative analyses show RNNs’ superiority in robustness and learning efficiency, with up to 46.9% improvement in classification tasks involving sensory fusion. We introduce a taxonomy of MSI tasks and a novel evaluation framework for model benchmarking. Real‐world case studies—from speech recognition to prosthetic control—highlight practical applications. Challenges in interpretability, data efficiency, and generalization are also discussed. The review provides actionable insights for future research in both computational neuroscience and artificial intelligence. By bridging neurobiological principles and machine learning, RNN‐based models pave the way for intelligent systems capable of flexible, context‐aware multisensory processing.