jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gabriela Dalmora | Eron Paulo Borges Filho | Angelo Antonio Maraschin Conterato | William Spenassato Roso | Carlos Eduardo Pereira | Aline Dettmer
Source: Results in Surfaces and Interfaces (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Methods of corrosion prevention for steel in marine environments: A review Corrosion in marine environments poses a challenge for steel and concrete structures due to constant exposure to corrosive elements such as saltwater, oxygen, chlorides, and microorganisms, particularly sulfate-reducing bacteria. These conditions accelerate material deterioration, reducing the durability of infrastructures. This review highlights advancements in corrosion prevention methods, focusing on innovations that include metallic and organic coatings, cementitious composites, and ecological inhibitors. Among the advancements, Zn–Al metallic coatings form sacrificial anodes that preferentially oxidize, protecting steel substrates, while the incorporation of epoxy and polyurethane layers provides impermeable barriers against chloride penetration and moisture ingress. High-ductility cementitious composites enhance resistance by minimizing crack propagation and promoting self-healing through the deposition of calcium carbonate and C–S–H gel. Superhydrophobic surfaces leverage nano and microscale interactions to repel water and prevent biofilm formation, maintaining functionality even after mechanical damage. Ecological inhibitors, such as sodium phosphate, interact chemically with steel surfaces to create protective films, achieving up to 91.7% corrosion inhibition. Additionally, artificial intelligence models have advanced the prediction of corrosion rates, enabling tailored protection strategies. By elucidating these mechanisms, this study highlights sustainable and efficient solutions for extending infrastructure lifespans and enhancing safety and economic viability in marine environments. Corrosion prevention ; Corrosion ; Forensic engineering ; Engineering ; Environmental science ; Metallurgy ; Materials science Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Smart Materials for Construction
Authors: Kübra Kaygisiz | Deborah Sementa | Vignesh Athiyarath | Xi Chen | Rein V. Ulijn
Source: Nature Reviews Materials (review, 2025-03-13)
Field: Materials Science — Biomaterials

Context dependence in assembly code for supramolecular peptide materials and systems Supramolecular chemistry ; Context (archaeology) ; Peptide ; Code (set theory) ; Nanotechnology ; Materials science ; Crystallography ; Chemistry ; Computer science ; Programming language ; Biology ; Crystal structure ; Biochemistry ; Paleontology Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Protein Structure and Dynamics
Authors: Gen Li | Zhihong Du | Chao Wu | Yawei Liu | Yan Xu | Roy Lavendomme | Shumin Liang | En‐Qing Gao | Dawei Zhang
Source: Nature Communications (article, 2025-01-09)
Field: Chemistry — Inorganic Chemistry

Charge-transfer complexation of coordination cages for enhanced photochromism and photocatalysis Intensified host-guest electronic interplay within stable metal-organic cages (MOCs) presents great opportunities for applications in stimuli response and photocatalysis. Zr-MOCs represent a type of robust discrete hosts for such a design, but their host-guest chemistry in solution is hampered by the limited solubility. Here, by using pyridinium-derived cationic ligands with tetrakis(3,5-bis(trifluoromethyl)phenyl)borate (BAr<sub>F</sub><sup>-</sup>) as solubilizing counteranions, we report the preparation of soluble Zr-MOCs of different shapes (1-4) that are otherwise inaccessible through a conventional method. Enforced arrangement of the multiple electron-deficient pyridinium groups into one cage (1) leads to magnified positive electrostatic field and electron-accepting strength in favor of hosting electron-donating anions, including halides and tetraarylborates. The strong charge-transfer (CT) interactions activate guest-to-host photoinduced electron transfer (PET), leading to pronounced and regulable photochromisms. Both ground-state and radical structures of host and host-guest complexes have been unambiguously characterized by X-ray crystallography. The CT-enhanced PET also enables the use of 1 as an efficient photocatalyst for aerobic oxidation of tetraarylborates into biaryls and phenols. This work presents the solution assembly of soluble Zr-MOCs from cationic ligands with the assistance of solubilizing anions and highlights the great potential of harnessing host-guest CT for boosting PET-based functions and applications. Photocatalysis ; Pyridinium ; Cationic polymerization ; Chemistry ; Electron transfer ; Photochemistry ; Radical ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Porphyrin and Phthalocyanine Chemistry
Authors: Ary Machado de Azevedo | Pedro Henrique Poubel Mendonça da Silveira | Thomaz Jacintho Lopes | Odilon Leite Barbosa da Costa | Sérgio Neves Monteiro | Valdir Florêncio da Veiga | Paulo Silveira | Domingos D’Oliveira Cardoso | André Ben‐Hur da Silva Figueiredo
Source: Polymers (review, 2025-04-19)
Field: Materials Science — Polymers and Plastics

Ionizing Radiation and Its Effects on Thermoplastic Polymers: An Overview This article explores the foundational principles of ionizing radiation and provides a comprehensive overview of its impact on thermoplastic polymers. Ionizing radiation, encompassing gamma rays, X-rays, and electron beams, has been extensively studied due to its capacity to alter the molecular structure of polymers. These changes enable advancements in various applications by promoting molecular crosslinking, controlled degradation, molecular grafting, and crystallinity adjustments. The article delves into the fundamental mechanisms of radiation thermoplastic polymer interactions, including ionization, electronic excitation, and free radical formation. It highlights how these processes lead to structural transformations that enhance the physical, thermal, and mechanical properties of thermoplastic polymers. Factors such as radiation type, absorbed doses, temperature, and environmental conditions are discussed in the context of their role in controlling these modifications. Key practical applications are identified across fields such as medicine, food packaging, aerospace, and industry. Examples include the production of sterilizable medical devices, enhanced food packaging for longer shelf life, and radiation-resistant materials for the aerospace and nuclear sectors. Despite its many advantages, the article also emphasizes challenges such as process variability, polymer sensitivity to radiation, and standardization difficulties. The review underscores emerging research directions, including optimizing irradiation parameters and integrating advanced characterization techniques like Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray diffraction (XRD). The development of new polymer blends and composites, designed for irradiation-induced property enhancement, represents a promising area of innovation. Materials science ; Thermoplastic ; Polymer ; Context (archaeology) ; Crystallinity ; Ionizing radiation ; Fourier transform infrared spectroscopy ; Nanotechnology ; Composite material ; Irradiation ; Chemical engineering ; Physics Polymer Nanocomposite Synthesis and Irradiation ; Photopolymerization techniques and applications ; Silicone and Siloxane Chemistry
Authors: Zhekun Zhang | Yankun Wang | Tianqi Guo | Pengfei Hu
Source: Catalysts (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

The Influence of Defect Engineering on the Electronic Structure of Active Centers on the Catalyst Surface Defect engineering has recently emerged as a cutting-edge discipline for precise modulation of electronic structures in nanomaterials, shifting the paradigm in nanoscience from passive ‘inherent defect tolerance’ to proactive ‘defect-controlled design’. The deliberate introduction of defect—including vacancies, dopants, and interfaces—breaks the rigid symmetry of crystalline lattices, enabling new pathways for optimizing catalysis performance. This review systematically summarizes the mechanisms underlying defect-mediated electronic structure at active sites regulation, including (1) reconstruction of the electronic density of states, (2) tuning of coordination microenvironments, (3) charge transfer and localization effects, (4) spin-state and magnetic coupling modulation, and (5) dynamic defect and interface engineering. These mechanisms elucidate how defect-induced electronic restructuring governs catalytic activity and selectivity. We further assess advanced characterization techniques and computational methodologies for probing defects-induced electronic states, offering deeper mechanistic insights at atomic scales. Finally, we highlight recent breakthroughs in defect-engineered nanomaterials for catalytic applications, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER) and beyond, while discussing existing challenges in scalability, defect stability, and structure–property causality. This review aims to provide actionable principles for the rational design of defects to tailor electronic structures toward next-generation energy technologies. Materials science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: S M Masfequier Rahman Swapno | S. M. Nuruzzaman Nobel | Md Babul Islam | Pronaya Bhattacharya | Ebrahim A. Mattar
Source: Neural Computing and Applications (article, 2025-01-20)
Field: Chemistry — Analytical Chemistry

ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. Computational Science and Engineering ; Transformer ; Artificial intelligence ; Computer science ; Machine learning ; Excitation ; Machine vision ; Pattern recognition (psychology) ; Engineering ; Electrical engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Zongliang Xie | Lu Fan | He Li | Zhaoyu Ran | Sha Lai | Xiaoyan Liu | Ashlin Deatherage | Yalin Wang | Qi Li | Yi Yin | Yi Liu
Source: Progress in Polymer Science (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Recent trends in all-organic polymer dielectrics for high-temperature electrostatic energy storage capacitors Capacitor ; Energy storage ; Dielectric ; Materials science ; Polymer ; Engineering physics ; Polymer capacitor ; Composite material ; Electrical engineering ; Optoelectronics ; Physics ; Voltage ; Engineering ; Thermodynamics ; Electrolytic capacitor Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Yujie Shi | Shijie Zhang | Hang Zhou | Dong Yue | Gang Liu | Wenshuai Ye | Renjie He | Guo Zhao
Source: Metals (article, 2025-01-17)
Field: Chemistry — Electrochemistry

Recent Developments in Heavy Metals Detection: Modified Electrodes, Pretreatment Methods, Prediction Models and Algorithms Heavy metal pollution has become an increasingly serious environmental issue, making the detection of heavy metals essential for safeguarding public health and the environment. This review aims to highlight the commonly used methods for detecting heavy metals (such as atomic absorption spectroscopy (AAS), atomic emission spectroscopy (AES), inductively coupled plasma–mass spectrometry (ICP-MS), square-wave anodic stripping voltammetry (SWASV), etc.), with a particular focus on electrochemical detection and electrode modification materials. Metal nanomaterials (such as titanium dioxide (TiO2), copper oxide (CuO), ZIF-8, MXene, etc.) are emphasized as promising candidates for enhancing the performance of sensors due to their high surface area and excellent catalytic properties. However, challenges such as interference from non-target heavy metal ions and the formation of organometallic complexes with organic compounds can complicate the detection process. To address these issues, two potential solutions have been proposed: the development of advanced algorithms (such as machine learning (ML), back-propagation neural network (BPNN), support vector machines (SVM), random forests (RF), etc.) for signal processing and the use of pretreatment methods (such as Fenton oxidation (FO), ozone oxidation, and photochemical oxidation) to suppress such interferences. This paper aims to review commonly used methods for detecting heavy metals, with a particular emphasis on electrochemical techniques. It will also highlight the challenges faced in these methods, such as interference and sensitivity limitations, and propose innovative solutions, including the use of metal nanomaterials for improved sensor performance and the integration of advanced algorithms and pretreatment techniques to address interference and enhance detection accuracy. Algorithm ; Heavy metals ; Computer science ; Electrode ; Materials science ; Chemistry ; Environmental chemistry ; Physical chemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Mahdi Rashvand | Yuqiao Ren | Da‐Wen Sun | Julia Senge | Christian Krupitzer | Tobi Fadiji | Marta Sanzo Miró | Alex Shenfield | Nicholas J. Watson | Hongwei Zhang
Source: Trends in Food Science & Technology (article, 2025-03-31)
Field: Chemistry — Analytical Chemistry

Artificial intelligence for prediction of shelf-life of various food products: Recent advances and ongoing challenges Accurate estimation of shelf-life is essential to maintain food safety, reduce wastage, and improve supply chain efficiency. Traditional methods such as microbial and chemical analysis, and sensory evaluation provide reproducible results but require time and labor and may not be suitable for real-time or high-throughput applications. The integration of artificial intelligence (AI) with advanced analysis techniques offers a suitable alternative for rapid, data-driven estimation of shelf-life in dynamic storage environments. The current review assesses the application of AI-based techniques such as machine learning (ML), deep learning (DL), and hybrid approaches in food product shelf life prediction. This study highlights how AI can be utilized to examine data from non-destructive testing methods like hyperspectral imaging, spectroscopy, machine vision, and electronic sensors to enhance predictive performance. The review also describes how AI-based techniques contribute to managing food quality, reduce economic losses, and enhance sustainability by ensuring optimized food distribution and reducing waste. AI techniques overcome conventional techniques by considering intricate, multi-sourced information capturing microbiological, biochemical, and environmental factors influencing food spoilage. Meat, dairy, fruits and vegetables, and beverage case studies illustrate AI techniques' superiority in real-time monitoring and quality assessment. It also identifies limitations such as data availability, model generalizability, and computational cost, constraining extensive applications. Cloud and Internet of Things (IoT) platform integration into future applications has to be considered to enable real-time decision-making and adaptive modeling. AI can be a paradigm-changing tool in food industries with intelligent, scalable, and low-cost interventions in food safety, waste reduction, and sustainability. • AI incorporates non-invasive methods for dynamic, real-time shelf-life forecasting. • Machine vision and spectroscopy improve shelf-life monitoring of fresh produce and meat. • Hybrid AI models enhance shelf-life prediction accuracy under dynamic storage conditions. • AI reduces food waste, energy use, and carbon emissions across supply chains. • Deep learning predicts spoilage by analyzing microbial, chemical, and environmental data. Shelf life ; Computer science ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Xianguang Zhao | Yixin Yu | Xudong Xu | Ziqi Zhang | Zhen Chen | Yubo Gao | Liang Zhong | Jiajie Chen | Jiaxin Huang | Jie Qin | Qingyun Zhang | Xuemei Tang | Dongqin Yang | Zhiling Zhu
Source: Advanced Materials (article, 2025-01-12)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted High‐Throughput Screening of Nanozymes for Ulcerative Colitis Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature of UC symptoms, current pharmacological treatments fail to meet patients' expectations. This study presents a machine learning-assisted high-throughput screening strategy to expedite the discovery of efficient nanozymes for UC treatment. Therapeutic requirements, including antioxidant property, acid stability, and zeta potential, are quantified and predicted by using a machine learning model. Non-quantifiable attributes, including intestinal barrier repair efficacy and biosafety, are assessed via high-throughput screening. Feature significance analysis, sure independence screening, and sparsifying operator symbolic regression reveal the high-dimensional structure-activity relationships between material features and therapeutic needs. SrDy<sub>2</sub>O<sub>4</sub> with high stability, low toxicity, targeting ability, and reactive oxygen species (ROS) scavenging capability is identified, which reduces ROS production, lowers cytochrome C levels in cytoplasm, and inhibits apoptosis in intestinal epithelial cells by stabilizing the mitochondrial membrane potential. Mice treated with SrDy<sub>2</sub>O<sub>4</sub> show improvements in colon length and body weight compared with dextran sodium sulfate salt-treated model group. Transcriptomic and 16S rRNA sequencing analyses show that SrDy<sub>2</sub>O<sub>4</sub> boosts beneficial gut bacteria, and decreases pathogenic bacteria, thereby effectively restoring gut microbiota balance. Moreover, SrDy<sub>2</sub>O<sub>4</sub> offers the advantage of X-ray imaging without side effects. Ulcerative colitis ; Reactive oxygen species ; High-throughput screening ; Throughput ; Biology ; Transcriptome ; Apoptosis ; Antioxidant ; Cell biology ; Bioinformatics ; Biochemistry ; Medicine ; Computer science ; Internal medicine ; Gene Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Ahmad Abulfathi Umar | Mohammad M. Hossain
Source: Fuel (review, 2025-01-10)
Field: Materials Science — Materials Chemistry

Hydrogen storage via adsorption: A review of recent advances and challenges Hydrogen storage ; Adsorption ; Nanotechnology ; Chemistry ; Biochemical engineering ; Materials science ; Hydrogen ; Organic chemistry ; Engineering Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems
Authors: Fan Liu | David R. Diercks | Praveen Kumar | Arim Seong | Mohammed Hussain Abdul Jabbar | Cenk Gümeci | Yoshihisa Furuya | Nilesh Dale | Takanori Oku | Masahiro Usuda | Pejman Kazempoor | Iman Ghamarian | Lin Liu | Liyang Fang | Di Chen | Zixian Wang | Stephen J. Skinner | Chuancheng Duan
Source: Science Advances (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Redesigning protonic ceramic electrochemical cells to lower the operating temperature Protonic ceramic electrochemical cells (PCECs) can operate at intermediate temperatures (450° to 600°C) for power generation and hydrogen production. However, the operating temperature is still too high to revolutionize ceramic electrochemical cell technology. Lowering the operating temperature to <450°C will enable a wider material choice and reduce system costs. We present approaches to redesigning PCECs via readily fabricated single-grain-thick, chemically homogeneous, and robust electrolytes and a nano-micro positive electrode. At 450°C, the PCECs achieve a peak power density of 1.6 watt per square centimeter on H<sub>2</sub> fuel, 0.5 watt per square centimeter on NH<sub>3</sub> fuel, and 0.3 watt per square centimeter on CH<sub>4</sub> fuel in fuel cell mode. In steam electrolysis mode, a current density of >0.6 ampere per square centimeter with a Faradaic efficiency of >90% is achievable at 1.4 volt and 400°C. In addition, exceptional durability (>2000 hours) has been demonstrated, with a degradation rate of <0.01 millivolt per 100 hours in fuel cell mode at 400°C. Materials science ; Ceramic ; Electrolysis ; Power density ; Electrolyte ; Faraday efficiency ; Electrolytic cell ; Volt ; Electrochemistry ; Electrode ; Electrochemical cell ; Chemical engineering ; Electrical engineering ; Composite material ; Chemistry ; Power (physics) ; Voltage ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Advanced battery technologies research