jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ying He | Houyu Tao | Yuanzhi Wang | Shunda Qiao | Chu Zhang | Hong Qi | Shiyu Hu | Wenxiang Huang | Dajiang Yu | Xuebo Zhang | Yufei Ma
Source: Laser & Photonics Review (article, 2026-01-18)
Field: Chemistry — Spectroscopy

Standoff LITES Sensor Based on a Frequency‐Stabilized Encased Quartz Tuning Fork ABSTRACT We propose and demonstrate a standoff light‐induced thermoelastic spectroscopy (standoff‐LITES) sensor for the first time. An encased quartz tuning fork (QTF) was employed in the standoff‐LITES sensor to improve the frequency stability. The QTF operating temperature was optimized to be ∼0°C. A distributed feedback (DFB) interband cascade laser (ICL) emitting at 3.27 µm was employed, whose emission wavenumber range of 3056.8–3058.1 cm −1 covers the absorption lines of water (H 2 O) and methane (CH 4 ). For the standoff gas sensing concept demonstration, the sensor employed an off‐axis parabolic mirror and a cooperative target positioned 5 m away from the mirror, creating a 10‐meter absorption path. The developed standoff‐LITES sensor system achieved minimum detection limits (MDLs) of 8.2 ppm for H 2 O and 1.6 ppb for CH 4 , corresponding to the NNEA of 6.7 × 10 −9 W·cm −1 Hz −1/2 and 6.3 × 10 −9 W·cm −1 Hz −1/2 , respectively. During a one‐week open‐field monitoring experiment, this sensor continuously measured the ambient H 2 O and CH 4 concentrations, and the acquired data were basically consistent with the observation data from the local meteorological station and atmospheric background station in terms of variation trends. These results verify that the standoff‐LITES sensor presented here possesses considerable application potential for long‐term standoff monitoring of atmospheric trace gases. Tuning fork ; Trace gas ; Photoacoustic spectroscopy ; Absorption (acoustics) ; Quartz ; Temperature measurement ; Cascade ; Methane ; Absorption spectroscopy Spectroscopy and Laser Applications ; Advanced Fiber Optic Sensors ; Acoustic Wave Resonator Technologies
Authors: Yucen Deng | Ziyu Liu | Xinyuan Zhu | Youfu Wang | Xuesong Feng | Jinghui Yang
Source: Theranostics (article, 2026-01-01)
Field: Materials Science — Biomaterials

Kidney-targeted nanoplatforms: Strategies and applications Renal diseases remain a major global health burden, with an estimated 850 million individuals affected by chronic kidney disease, acute kidney injury, glomerulonephritis, and diabetic nephropathy. These multifactorial diseases collectively account for substantial morbidity and mortality burdens. This grim trajectory demands urgent development of drugs that are capable of simultaneously enhancing renal efficacy while circumventing systemic toxicity. In response to this challenge, engineered nanoplatforms designed specifically for the treatment of kidney diseases have emerged as a promising solution. These nanoplatforms offer the unique ability to deliver targeted therapeutics directly to specific regions of the kidney, thereby improving drug efficacy while reducing off-target effects. Unlike the well-established oncological applications of nanomedicine, renal-specific formulations remain in their developmental nascency. Nevertheless, accumulating preclinical evidence indicates that nanotherapeutics hold significant promise for improving the clinical management of kidney diseases through targeted and mechanism-based interventions. The nephrotropic mechanisms and structural determinants of renal nanoplatforms fundamentally diverge from those of conventional nanotherapeutics. Therefore, a thorough understanding of the principles governing renal targeting is essential for designing nanomedicines that achieve precise kidney-specific delivery while ensuring biosafety. In this review, we summarize the current understanding of structure-function relationships that govern the targeting efficiency and biodistribution of nanoparticles in the kidney, with a focus on passive targeting mechanisms driven by key physicochemical parameters, such as particle size, surface charge, shape, and density, as well as active targeting strategies based on specific receptor-ligand interactions. Biodistribution ; Targeted drug delivery ; Drug delivery ; Nanomedicine ; Kidney ; Kidney disease ; Personalized medicine ; Drug Nanoparticle-Based Drug Delivery ; Chemotherapy-induced organ toxicity mitigation ; Acute Kidney Injury Research
Authors: Rui Qin | Huanzheng Zhu | Rongxuan Zhu | Pintu Ghosh | Min Qiu | Qiang Li
Source: Light Science & Applications (article, 2026-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-dimensional camouflage against VIS-NIR hyperspectral, MIR intensity, and MIR polarization imaging Camouflage is essential in modern security and military operations, playing a critical role in evading detection and enhancing the survivability of equipment. However, most existing camouflage devices operate in a single dimension, rendering them inadequate against emerging multi-dimensional detection techniques, including visible to near-infrared (VIS-NIR) hyperspectral imaging and mid-infrared (MIR) polarization imaging. In this work, we propose a multi-dimensional camouflage strategy that realizes simultaneous VIS-NIR spectrum camouflage, MIR intensity, and polarization camouflage by a hierarchical structure. The multi-dimensional camouflage device exhibits an emissivity of 0.7, a low degree of linear polarization (< 1.5%) at large observation angles in MIR range, and high spectral similarity (>96.9%) in the VIS-NIR range. Moreover, it deceives hyperspectral classification in vegetative background and blends into its environment under MIR intensity and polarization imaging. This work introduces a novel paradigm for multi-dimensional camouflage techniques and opens up new avenues for electromagnetic waves manipulation. Camouflage ; Hyperspectral imaging ; Polarization (electrochemistry) ; Rendering (computer graphics) ; Electromagnetic spectrum ; Spectral imaging ; Electromagnetic radiation ; Polarimetry Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Optical Polarization and Ellipsometry
Authors: Nikhil Kumar | Pranav Anunay | Sunil Kumar | Lalit Kumar Meena | Dharmender Singh
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

An overview on emerging green organic corrosion inhibitors: sustainable solution for oil and gas industrial applications Corrosion remains a significant challenge across various industrial sectors, resulting in substantial economic losses, environmental hazards, and safety risks. Traditional corrosion inhibitors, though effective, often pose toxicity and environmental concerns, prompting the need for sustainable alternatives. The demand for eco-friendly and sustainable chemical species is increasing as industries seek to replace conventional toxic substances. This review emphasizes organic inhibitors derived from natural sources, such as amino acids, biopolymers, plant extracts, and environmentally benign synthetic compounds. Additionally, compounds synthesized through ultrasound and microwave irradiation, polyethylene glycols, ionic liquids, and multicomponent reactions are also considered environmentally friendly potential candidates to inhibit corrosion. The chemicals or compounds synthesized using water, ionic liquids, and supercritical carbon dioxide are also green corrosion inhibitors (CIs). Industrial processes such as oil extraction and gas transportation, where equipment are exposed to highly acidic environments, face significant corrosion challenges. Therefore, organic, green, eco-friendly, and cost-effective CIs are the primary demands of industry, researchers, and society. The development of new efficient green CIs can lead to the current research towards a sustainable pathway for future generations. In this context, researchers are continually working to develop more efficient CIs with the least hazardous environmental effects. The mechanisms of action, structural characteristics, and adsorption behaviour of OCIs are discussed in detail, along with their applicability in corrosive environments, such as oil wells, pipelines, and marine systems. This review provides a comprehensive overview of corrosion fundamentals, different classes of OCIs, key factors responsible for corrosion inhibition, mechanistic pathways, molecular modelling, and artificial neural networks for determining the rate of corrosion, followed by a future prospective. This review aims to support ongoing research efforts towards the sustainable corrosion control, offering valuable insights for scientists, engineers, and industries striving for greener and more cost-effective corrosion mitigation strategies. Corrosion ; Environmentally friendly ; Hazardous waste ; Petrochemical ; Petroleum industry ; Adsorption ; Chemical industry ; Ionic liquid ; Petroleum Corrosion Behavior and Inhibition ; Polymer composites and self-healing ; Synthesis of Tetrazole Derivatives
Authors: Dorothee Wagner | Leander Spierling | Oliver S. Wenger
Source: ACS Central Science (article, 2026-01-07)
Field: Chemistry — Organic Chemistry

Expanding Thermodynamic and Kinetic Frontiers in Molecular Photocatalysis Visible photons carry significantly more energy than the thermal energies typically used to overcome activation barriers in conventional chemistry. This thermodynamic advantage enables photochemical reactions that are inaccessible from electronic ground states. However, photochemistry also faces a kinetic challenge: excited states are inherently short-lived, necessitating rapid reactivity before their decay. In this Outlook, we explore the unique interplay of thermodynamics and kinetics in molecular photochemistry. We highlight current limits and knowledge gaps and propose directions for advancing the conceptual framework of photocatalysis. Topics include the design of photocatalysts with extreme redox potentials, the use of solvated electrons and visible-to-UV upconversion, and the potential to bypass Kasha's rule for higher-energy photochemical processes. Our aim is to survey strategies for pushing the boundaries of photocatalysis and to inspire future conceptual innovation in the field. Photocatalysis ; Excited state ; Photon ; Kinetic energy ; Kinetics ; Reactivity (psychology) ; Redox ; Electron Radical Photochemical Reactions ; Chemical Reactions and Mechanisms ; Advanced Photocatalysis Techniques
Authors: Dongwon Shin | Jiwoong Choi | Byeongmin Park | Sun Hwa Kim | Man Kyu Shim
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Biomaterials

Factors governing tumor penetration of nanomedicines: intrinsic and extrinsic determinants Nanomedicine-based drug delivery has shown considerable promise for cancer therapy; however, the efficient transport and deep penetration of nanoparticles within solid tumors remain major challenges for clinical translation. These limitations arise from the combined effects of nanoparticle physicochemical properties and biological transport barriers acting across multiple biological scales, rather than being governed by any single isolated parameter. Accordingly, nanoparticle transport and tumor penetration cannot be adequately understood by considering individual parameters in isolation, but instead reflect the combined contributions of intrinsic determinants related to nanoparticle properties and extrinsic determinants associated with penetration-enhancing interventions, underscoring the need for an integrated framework that explicitly distinguishes and organizes these two classes of factors when evaluating nanoparticle transport <i>in vivo</i>. In this review, we provide a structured overview of the intrinsic and extrinsic determinants that regulate nanoparticle transport <i>in vivo</i>. First, we summarize intrinsic nanoparticle-related factors and discuss how their physicochemical properties influence key transport processes, including systemic circulation, tumor accumulation and penetration, cellular uptake, and excretion. Second, we review advanced engineering strategies designed to enhance tissue penetration, such as size- and charge-transformable nanoplatforms that aim to balance prolonged circulation with improved intratumoral distribution. Third, we outline extrinsic intervention-based approaches, including external stimuli and pharmacological modulators, that improve nanoparticle penetration by modulating transport barriers within solid tumors. Finally, we discuss remaining challenges and knowledge gaps that limit the predictability and reproducibility of nanoparticle transport <i>in vivo</i>, and provide perspectives on how a systems-level understanding of these interdependent factors can support the clinical translation of nanomedicines. Nanoparticle ; Penetration (warfare) ; Nanomedicine ; Interdependence ; Drug delivery ; Systemic circulation Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Advanced Drug Delivery Systems
Authors: Alec Lamens | Jürgen Bajorath
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Explainable artificial intelligence for molecular design in pharmaceutical research The rise of artificial intelligence (AI) has taken machine learning (ML) in molecular design to a new level. As ML increasingly relies on complex deep learning frameworks, the inability to understand predictions of black-box models has become a topical issue. Consequently, there is strong interest in the field of explainable AI (XAI) to bridge the gap between black-box models and the acceptance of their predictions, especially at interfaces with experimental disciplines. Therefore, XAI methods must go beyond extracting learning patterns from ML models and present explanations of predictions in a human-centered, transparent, and interpretable manner. In this Perspective, we examine current challenges and opportunities for XAI in molecular design and evaluate the benefits of incorporating domain-specific knowledge into XAI approaches for model refinement, experimental design, and hypothesis testing. In this context, we also discuss the current limitations in evaluating results from chemical language models that are increasingly used in molecular design and drug discovery. Field (mathematics) ; Bridge (graph theory) ; Deep learning ; Current (fluid) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Yingjun Chen | Weiwei Xue
Source: ACS Chemical Neuroscience (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Machine Learning for <i>De Novo</i> Molecular Generation: A Comprehensive Review Deep generative models have emerged as powerful computational engines for <i>de novo</i> molecular design, enabling efficient exploration of a vast chemical space that remains inaccessible to traditional experimental approaches. This review provides a comprehensive survey of machine learning-driven molecular generation, systematically organizing the field across three foundational pillars: molecular representations, model architectures, and evaluation frameworks. We present a detailed taxonomy of state-of-the-art generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Transformers, Diffusion Models, Normalizing Flows, and Hybrid Architectures, analyzing their underlying mechanisms, comparative strengths, and inherent limitations. Critically, we depart from purely descriptive surveys by systematically examining algorithmic failure modes and practical deployment challenges across model families. We discuss core applications spanning distribution learning and goal-directed generation. Special attention is given to challenging therapeutic domains such as Central Nervous System (CNS) drug discovery, where stringent constraints like blood-brain barrier (BBB) permeability and neurotoxicity mitigation demand multiparameter optimization. We critically evaluate the gap between computational benchmarks and practical medicinal chemistry, addressing synthetic feasibility and experimental validation. Subsequently, we highlight persistent theoretical, computational, and empirical challenges that currently limit widespread deployment, and outline promising future opportunities, including physics-informed architectures, large language models, and autonomous laboratories. This review aims to provide actionable insights for both machine learning researchers and medicinal chemists engaged in next-generation drug discovery. Generative grammar ; Adversarial system ; Field (mathematics) ; Software deployment ; Deep learning ; Profiling (computer programming) ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Healthcare
Authors: Linfan Sun | Haoyu Zhang | Zhiduo Liu | Jun Cheng | Ge Zhou | Ximin Zang | Lijia Chen | Peter K. Liaw
Source: Materials Research Letters (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Physical-information machine learning for strength and ductility prediction of metastable <i>β</i> titanium alloys Machine learning for predicting metastable β titanium alloy properties suffers from limited generalisation, unreliable extrapolation beyond data boundaries, and inadequate modelling of nonlinear effects of process parameters. This study proposes a physics-informed machine learning approach incorporating intrinsic physical attributes and phase transformation kinetics. Using 496 samples, it achieves R2 = 0.95 for ultimate tensile strength (UTS) and 0.90 for elongation (El), accurately predicting out-of-boundary alloys with similar heat treatment responses within 5.0% UTS and 2.5% El error. This approach reduces reliance on data completeness and complex algorithms, providing an accurate, generalisable path to UTS- and El-targeted titanium alloy design. Titanium alloy ; Extrapolation ; Ultimate tensile strength ; Ductility (Earth science) ; Titanium ; Metastability ; Alloy ; Elongation Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Model Reduction and Neural Networks
Authors: Aayzaz Ahmed | Mirza Aamish Hassan Khan | George Uwadiegwu Alaneme | Tariq Ali | Muhammad Zeeshan Qureshi | Ayed Eid Alluqmani | Ali Ajwad
Source: Separation and Purification Technology (article, 2026-01-03)
Field: Chemistry — Inorganic Chemistry

Metal-organic frameworks: Role of artificial intelligence and machine learning algorithms for efficient discovery, design, synthesis and prediction of CO2 capture capacity - A state of art review Scalability ; Stability (learning theory) ; Support vector machine ; Feature (linguistics) ; Key (lock) ; State (computer science) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Abhilash Purohit | Alok Agrawal | Priyabrat Pradhan | Arvind Kumar | Suresh Palanimuthu | Pankaj Kumar Chauhan
Source: Wood Material Science and Engineering (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Machine learning guided optimization of engineered wood dust reinforced hybrid polymer composites Polymer ; Composite number ; Engineered wood ; Filler (materials) Machine Learning in Materials Science ; Natural Fiber Reinforced Composites ; Topology Optimization in Engineering
Authors: Fei Tan | Zixuan Zhao | Yanbin Jiang | Wenchao Zhang | Tong Xie | Wei Chen | Muzhi Ma | Yangfan Liu | Yanpeng Ye | Zhu Xiao | Qian Lei | Guofu Xu | Jie REN | Yuyuan Zhao | Zhou Li
Source: Acta Materialia (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

Dual engine-driven strategy for advanced copper alloy design employing large language models Interpretability ; Alloy ; Dual (grammatical number) ; Microstructure ; Artificial neural network ; Precipitation ; Ultimate tensile strength Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Model Reduction and Neural Networks
Authors: Zhiqing Zhang | Dongmei Li | Chuan Liu | Zibin Yin | Wei Guan | Y Wang | Kai Lu | Wensheng Yu | Mingzhang Pan
Source: Case Studies in Thermal Engineering (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Multi-objective optimization of urea injection parameters of selective catalytic reduction system based SSABP-NSGA-II-TOPSIS Selective catalyst reduction (SCR) system efficiency is significantly affected by issues like uneven ammonia distribution and incomplete urea decomposition, which are closely related to urea injection parameters. Initially, a 3D CFD model was developed to analyze how spray angle, offset angle, the distance between the nozzle and the front section of the catalyst, and injection velocity affect NOx conversion and NH3 uniformity. Then, a hybrid optimization framework combining SSABP neural network with NSGA-II and TOPSIS is used to optimize the impact of four parameters on the SCR system. The SSABP surrogate model showed good predictive accuracy compared with CFD simulations, with error metrics such as Mean Absolute Error (MAE) remaining within acceptable limits. The optimal injection parameters were identified as a spray angle of 35°, an offset angle of 2.5°, a nozzle-to-catalyst distance of 5.7 times the pipe diameter, and an injection velocity of 27 m/s. Under these conditions, the NOx conversion efficiency increased by 9.15 %, and the NH3 uniformity index improved from 0.71 to 0.845. The results demonstrate that the proposed SSABP–NSGA-II–TOPSIS framework is effective for optimizing urea injection strategies and enhancing SCR system performance under the investigated steady-state operating condition. Nozzle ; Offset (computer science) ; NOx ; Selective catalytic reduction ; Artificial neural network ; Urea ; Computational fluid dynamics ; Ammonia ; Approximation error Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Ammonia Synthesis and Nitrogen Reduction
Authors: Ximing Zhang | Xingyu Hao | Guoke Wei | Hang Zhang | Wei Chen | Feihu Shan | Kui Liu | Shujuan Tan | Guangbin Ji
Source: Composites Part B Engineering (article, 2026-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial neural network-assisted design of 3D printed moth-eye inspired metastructure for broadband microwave absorption and infrared stealth Microwave ; Reflection loss ; Broadband ; Absorption (acoustics) ; Bandwidth (computing) ; Infrared ; Emissivity ; Reflection (computer programming) Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies