jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amal A. Nassar | Ayman K. El‐Sawaf | Mahmoud F. Mubarak | Rasha Hosny
Source: ACS ES&T Water (article, 2026-01-13)
Field: Materials Science — Biomaterials

Eco-Friendly Scale Inhibition Strategies for Produced Water Management in Enhanced Oil Recovery: A Comprehensive Review Mineral scale formation remains a major challenge in the management of produced water during enhanced oil recovery (EOR) operations. Although conventional scale inhibitors such as phosphonates and polycarboxylates are effective, they are becoming increasingly undesirable due to their limited biodegradability and long-term environmental persistence. This review critically examines recent advances in environmentally friendly scale inhibition strategies, including biodegradable polymers, plant-based additives, nanostructured materials, and hybrid formulations. Particular emphasis is placed on their performance under the high-temperature and high-salinity conditions characteristic of EOR systems as well as their compatibility with polymers and surfactants used in chemical flooding processes. The mechanisms of scale control─such as nucleation delay, crystal distortion, particle dispersion, and surface passivation─are discussed based on laboratory investigations and reported field-scale applications. In addition, the review highlights the growing role of data-driven control strategies, predictive dosing, and digital monitoring in optimizing inhibitor deployment. Field-scale evidence from the literature indicates that eco-friendly and nanotechnology-enhanced inhibitors can reduce scaling-related downtime by approximately 20–40% while simultaneously decreasing chemical consumption by 15–30% under harsh EOR conditions. Overall, this work provides a structured framework linking environmental sustainability with materials innovation, offering guidance for the development of next-generation scale inhibitors that satisfy both operational demands and regulatory requirements in modern petroleum engineering. Sustainability ; Petroleum ; Scale (ratio) ; Enhanced oil recovery ; Downtime ; Compatibility (geochemistry) ; Produced water Calcium Carbonate Crystallization and Inhibition ; Enhanced Oil Recovery Techniques ; Surfactants and Colloidal Systems
Authors: Abhishek Kansal | Nitin Dixit | Sanjeev Kumar | Akshay Dvivedi | Pradeep Kuamr
Source: Journal of Materials Science (article, 2026-01-09)
Field: Materials Science — Biomaterials

Review: zinc alloys for biomedical applications—alloy design, processing techniques, and bio-functional performance Corrosion ; Alloy ; Solid mechanics ; Zinc ; Titanium alloy ; Materials processing Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Science
Authors: Chandan Kumar Singh | Pran Kishore Deb
Source: AAPS PharmSciTech (article, 2026-04-02)
Field: Materials Science — Biomaterials

Polymer Drug Conjugate: A Revolution in Drug Delivery Drug delivery ; Drug ; Polymer ; Pharmaceutical technology ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; Cancer Research and Treatment ; Advanced Drug Delivery Systems
Authors: Yitao He | Jiří Červenka
Source: The Journal of Physical Chemistry C (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes Electrochemistry lies at the heart of modern energy technologies, yet connecting atomic-level insights to macroscopic performance remains an enduring challenge. Quantum-based simulations, such as density functional theory, have illuminated many fundamental processes, but their reach is limited by the complexity of real electrochemical environments. Bridging these scales requires a new conceptual framework that can expose the hidden connections between theory and experiment. Here, we argue that the thoughtful integration of artificial intelligence (AI) can transform electrochemical research by unifying theory, experiment, and data-driven inference. AI-assisted frameworks can accelerate convergence between computation and experiment, revealing hidden physical relationships and enabling closed-loop discovery. Realizing this vision will require developing transparent, interpretable AI models that earn the same scientific trust as human reasoning, unlocking deeper understanding and innovation across the electrochemical sciences. Bridging (networking) ; Orientation (vector space) ; Computation ; Electrode ; Electrochemistry ; Density functional theory Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Yuanying Ni | Yuening Wang | Meng Xq | Jie Lin | Xiaotian Wang
Source: Analysis & Sensing (article, 2026-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

QDs‐Based SERS Technology: Breakthroughs and Prospects From Biosensing to Food Safety Quantum dots (QDs) have emerged as a promising substrate in surface‐enhanced Raman scattering (SERS), due to their quantum confinement effects and tunable optical properties. QDs provide a new solution to the limitations faced by SERS technology, such as poor stability, high cost, and biological toxicity, and demonstrate promising application prospects. This review summarizes recent progress in QDs‐based SERS, including synthesis, enhancement mechanisms, and applications. In terms of synthesis strategies, various approaches such as chemical, physical, and biological ones are commonly employed to prepare quantum dots, in order to control their size, morphology, and surface functionalization. In terms of the enhancement mechanism, QDs‐based SERS substrate typically combines the advantages of electromagnetic field enhancement and chemical enhancement. For instance, by taking advantage of the quantum size effect and oxygen vacancy engineering to dynamically adjust the local surface plasmon resonance, highly efficient electromagnetic hotspots can be generated in QD semiconductors or in the composite structures of noble metals/QDs. Meanwhile, the quantized energy levels enhance selectivity through resonant charge transfer. Importantly, QDs‐based SERS substrate have been applied in wide fields including label‐free biosensing of cancer cells, virus identification, multimodal tumor diagnosis, and food safety detection, etc. Despite these advancements, QDs‐based SERS substrates still face challenges in terms of anti‐interference ability, scalability in manufacturing, and long‐term stability. And we propose that enhancing the selectivity and stability of QDs‐SERS substrates through methods such as surface modification is an important approach to improving their SERS activity. Additionally, integrating artificial intelligence with composite material design is expected to accelerate its industrialization and sustainable application in the fields of medicine and food safety. Quantum dot ; Biosensor ; Raman scattering ; Substrate (aquarium) ; Plasmon ; Photonics ; Field (mathematics) Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Laser-Ablation Synthesis of Nanoparticles
Authors: Yitao He | Jiří Červenka
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes Orientation (vector space) ; Platinum ; Electrode ; Electrochemistry ; Molecule Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Yongchul G. Chung | Myoung Soo Lah
Source: arXiv (Cornell University) (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

Hunting Structural Demons in Digital Reticular Chemistry Digital reticular chemistry relies on accurate crystal structures to power computational screening, data-driven discovery, and structure-property analysis, yet recent studies reveal that more than half of the top-performing candidates in major computational screening campaigns are chemically invalid. In experimental MOF databases, structural errors arise when disordered or incomplete structural models are incorrectly converted into fully specified simulation inputs. In hypothetical MOF database, structures are complete by construction but may encode chemically implausible oxidation states, coordination environments, or charge distributions. We term these erroneous structural models "structural demons." This mini-review asks three questions: where these errors enter, how we find them, and how we prevent them. On the prevention side, the key steps are keeping diffraction data and synthesis details together from the start, using consistent curation when structures enter a database, and filtering topology choices before structure generation. Connecting these steps can keep many bad structures out of downstream databases and reduce the need to fix them later. ENCODE ; Phaser ; Term (time) ; Reticular connective tissue ; Topology (electrical circuits) ; Key (lock) ; Crystal structure ; Computational model Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Nicholas N. Dahlen | Tori Z. Forbes
Source: Chemistry - Methods (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Democratizing AI‐Enhanced Experimental Optimization: An Accessible Workflow for Chemists Experimental optimization is a cornerstone of chemical research, driving the perpetual search for novel methods to accelerate the process. Statistical design of experiments (DoE) methodologies are tools designed to accelerate experimental optimization but traditionally struggle to incorporate the qualitative observational data that are often paramount to optimization. The rapid advancement of artificial intelligence (AI), particularly general‐use large language models (LLMs), presents a new avenue to address this limitation. We present an iterative AI‐enhanced DoE workflow that utilizes accessible AI tools available for minimal cost. In this workflow, an LLM digitizes handwritten lab notes into machine‐readable Markdown format. NotebookLM then consolidates observational data and integrates them into a prompt used by an LLM to perform a retrieval‐augmented generation (RAG) analysis. The result is a detailed research report grounded in the user‐provided information to provide insight into the chemical space, relevant literature to investigate, and experimental suggestions toward the goal of optimization. Finally, a human‐in‐the‐loop (HITL) review validates the AI‐generated report to ensure accuracy, relevance, and chemical safety. This approach aims to effectively bridge the gap between statistical DoE and qualitative insights, democratizing access to AI‐assisted experimental optimization. Workflow ; Cornerstone ; Bridge (graph theory) ; Observational study ; Documentation ; Experimental data ; Data collection ; Statistical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Shadma Afzal | Mariyam Fatima | Monalisha Mishra | Nidhi Shukla | Nand K. Singh | Mohammad Sarraf | Anshu Rastogi
Source: Plant Nano Biology (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Insights into progress in biogenic synthesis of nanomaterials: A review Biogenic synthesis of nanomaterials (NMs) has attracted significant attention as an eco-friendly, cost-effective, and sustainable alternative to conventional physical and chemical methods. This review presents a comprehensive analysis of the recent advancements in the biosynthesis of metal and metal and metal oxide NMs, quantum dots, and nanozyme using a wide array of biological systems. These include bacteria, fungi, yeast, algae (macro and micro), viruses, plants, food additives, and biodegradable waste extracts. These biological entities act as natural nanofactories, utilizing bioactive compounds such as enzymes, proteins, and secondary metabolites as reducing, capping and stabilizing agents to control NMs formation with varied morphologies, compositions and functional properties. The review delves into the mechanisms of intracellular and extracellular synthesis, highlighting the advantages of biogenic methods in terms of scalability, low toxicity, and biocompatibility. It highlights a wide range applications of biologically synthesized NMs in antimicrobial therapy, cancer treatment, catalysis, environmental remediation, and biosensing. Special emphasis is placed on the emerging role of food additives and biodegradable wastes as sustainable resources for nanomaterial (NM) production, promoting circular economy practices. Despite numerous advancements, challenges such as variability in biological sources, limited control over NM shape and size, and difficulties in standardization and scale-up remain. This review proposes the integration of artificial intelligence (AI), bioinformatics, and nanobiotechnology as promising avenues to optimize biosynthesis processes, enhance reproducibility, and accelerate the discovery of next-generation NMs. By bridging biological science with technological innovation, biogenic synthesis holds immense promise for addressing global challenges in healthcare, environmental sustainability and green manufacturing. • Biogenic methods improve safety, biocompatibility, and scalability • Microbes, plants, and algae serve as natural nanomaterial factories • Food waste and additives enable sustainable nanomaterial creation • Biogenic nanomaterials show promise in health and environmental care • Variability and scale-up remain key hurdles in green nanotechnology • AI and bioinformatics can optimize and speed up green material design Process (computing) ; Feature (linguistics) ; Natural (archaeology) ; Production (economics) Nanoparticles: synthesis and applications ; Magnesium Oxide Properties and Applications ; Graphene and Nanomaterials Applications
Authors: Malachy Stephen
Source: Journal of Applied Sciences and Environmental Management (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Nanoscale Energy Transport and Phonon Engineering. A Review Nanoscale energy transport and phonon engineering have become central topics in modern thermal science due to the breakdown of classical heat conduction models at reduced dimensions. In nanoscale systems, energy transport is governed by ballistic and quasi-ballistic phonon dynamics rather than purely diffusive Fourier behavior, necessitating advanced theoretical frameworks such as the Boltzmann transport equation, molecular dynamics, and density functional theory. This review provides a comprehensive overview of phonon-mediated heat transport, emphasizing scattering mechanisms, mean free path effects, and interface-driven thermal resistance. It highlights recent advances in phonon engineering strategies, including nanostructuring, alloying, phononic crystals, and low-dimensional materials, for controlling thermal conductivity. Experimental techniques such as time-domain thermoreflectance and Raman spectroscopy are discussed alongside computational and data-driven approaches. Applications in thermoelectrics, nanoelectronics, and energy systems are explored, demonstrating the importance of thermal control at the atomic scale. Finally, key challenges and future directions in nanoscale thermal management are outlined Phonon ; Thermal conduction ; Boltzmann equation ; Nanoscopic scale ; Ballistic conduction ; Mean free path ; Thermal ; Scattering ; Thermal energy Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Eunji Hwang | Heemyoung Hong | Yongjoon Lee | Yanggeun Joo | Sangsu Yer | Suyeon Cho | Heejun Yang
Source: Nature Reviews Materials (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Van der Waals materials for energy-efficient electronic devices van der Waals force ; Electronics ; Transistor ; Electronic materials ; Key (lock) ; Range (aeronautics) ; Bridge (graph theory) 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Ahmed A. Alajlouni | Sajjad Boorghan Farahan | Mohammad Mohammad Hosseini | Jingzhou Zhao
Source: The International Journal of Advanced Manufacturing Technology (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

A high throughput experimental system for the continuous casting of glass-coated microwires Scalability ; Throughput ; Casting ; Nanoscopic scale ; Process (computing) ; Magnetohydrodynamic drive ; Resource (disambiguation) ; Jet (fluid) Solidification and crystal growth phenomena ; Metallic Glasses and Amorphous Alloys ; Nanofluid Flow and Heat Transfer
Authors: Mokhtar Hjiri | N. MUSTAPHA | L. Chouiref
Source: Journal of Materials Science Materials in Electronics (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Engineered metal oxide nanostructures via sol–gel chemistry for next-generation spintronic devices Spintronics ; Fabrication ; Dopant ; Oxide ; Nanomaterials ; Heterojunction ; Grain boundary ; Nanostructure ZnO doping and properties ; Magnetic Properties and Synthesis of Ferrites ; Copper-based nanomaterials and applications
Authors: Asmaa S. F. Gbiliy | Ibrahim Abdelhai Senosy | Marwa A.A. Saadan | Mahmoud Sayed | I.P.W.A.V. Kumara | Ge-Fei Hao | Man-Qun Wang
Source: Chemical Engineering Journal (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Highly sensitive colorimetric 3D covalent organic framework sensors with artificial intelligence analysis enabled early detection of plant diseases Covalent bond ; Optical sensing ; Chemical sensor ; Covalent organic framework ; Colorimetry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Francesco Paesani | Richa Rashmi | Henry Agnew | Xuanyu Zhu | Ethan F. Bull-Vulpe | Ruihan Zhou
Source: Annual Review of Physical Chemistry (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

From Potentials to Properties: Data-Driven Many-Body Simulations of Water and Aqueous Systems Water is one of the most extensively studied molecular systems, yet its behavior across different phases, interfaces, and chemical environments continues to challenge existing models. Over the past decade, the development of data-driven many-body potential energy functions (PEFs) within the many-body energy (MB-nrg) formalism has enabled simulations of water and aqueous systems with unprecedented predictive power. Rooted in the many-body expansion and rigorously derived from "gold standard" electronic structure data, these PEFs bridge quantum chemistry and statistical mechanics within a unified framework. In this article, we review how the MB-pol PEF for water and the MB-nrg PEFs for hydrated halide and alkali metal ions have reshaped our understanding of aqueous properties across the gas, liquid, and solid phases, offering detailed insights into hydrogen bonding, spectroscopy, isotope effects, and phase stability. By connecting length scales and timescales while maintaining quantum-mechanical accuracy, the data-driven many-body MB-nrg formalism provides a robust foundation for realistic molecular simulations and offers new opportunities for addressing long-standing questions in physical chemistry and beyond. Halide ; Formalism (music) ; Aqueous solution ; Molecular dynamics ; Alkali metal ; Water chemistry ; Statistical mechanics ; Ion Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Gyoung S. Na | Chanyoung Park
Source: arXiv (Cornell University) (preprint, 2026-02-06)
Field: Materials Science — Materials Chemistry

Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics Various representation learning methods for molecular structures have been devised to accelerate data-driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom-level information, which is not sufficient to describe real-world molecular physics. Although electron-level information can provide fundamental knowledge about chemical compounds beyond the atom-level information, obtaining the electron-level information in real-world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. The proposed method achieved state-of-the-art prediction accuracy on extensive benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL. Representation (politics) ; Benchmark (surveying) ; Computation ; Code (set theory) ; Source code ; Molecule Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Qianqian Zhong | Qing Tan | Li-Dong Zhao
Source: Journal of Materiomics (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Synergistic optimization of SnSe/SnS thermoelectrics: from material breakthroughs to device integration • This perspective critically analyzes the bottlenecks hindering SnSe/SnS thermoelectrics, focusing on the single-crystal to polycrystalline performance gap and n-p type asymmetry. • A holistic, four-pillar roadmap for synergistic optimization is proposed, encompassing advanced polycrystalline engineering, accelerated n-type development, AI-driven discovery, and device-centric integration. • The roadmap provides a strategic guide to bridge the gap between laboratory material breakthroughs and commercial thermoelectric device application. System integration ; Work (physics) Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Copper-based nanomaterials and applications
Authors: Shani Zev | Michal E. Roth | Jishnu Narayanan S J | Dan Thomas Major
Source: Journal of Chemical Theory and Computation (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

RxnNet: An AI Framework for Reaction Mechanism Discovery─A Case Study of Carbocations Understanding complex chemical reaction cascades remains a major challenge in chemistry. Scalable investigation of their thermodynamic and kinetic properties requires the use of automated reaction prediction tools, which is a rapidly growing area in the study of chemical reactivity. However, the systematic exploration of intricate reaction networks involving highly reactive intermediates continues to pose significant difficulties. Here, we introduce RxnNet, a novel artificial intelligence-assisted platform for the automated prediction of chemical reaction mechanisms. RxnNet integrates heuristic rules with domain-specific chemical knowledge including stereochemistry, regiochemistry, conformational preferences, and isotope labeling, to construct mechanistically informed reaction networks. These networks are represented as graphs and are coupled with on-the-fly quantum chemical evaluations to identify all feasible intermediates and transition states. In this work, we apply RxnNet to carbocation chemistry, a notoriously complex and computationally demanding type of reaction. We demonstrate the method's capabilities by analyzing three multistep reactions with known mechanisms, each of which poses significant challenges even for expert computational and synthetic chemists. RxnNet provides a robust approach for uncovering reaction mechanisms, which can accelerate the understanding and design of transformations in complex chemical systems. Quantum chemical ; Scalability ; Heuristic ; Chemical reaction ; Carbocation ; Construct (python library) ; Chemical database ; Reaction mechanism Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis