jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amneesh Singla | Anubha Kumari | Aditya Gautam | Muskan | Avijit Das | Lata Rani | Anjali Sharma | Amiya Das | Neha Kanojia | Ajmer Singh Grewal
Source: Current Drug Therapy (article, 2026-03-27)
Field: Materials Science — Biomaterials

Smart Nanocarriers for Lung Cancer: Progress in Targeted Therapy,Controlled Release, and Theranostics Introduction/Objective: Lung cancer remains one of the leading causes of cancerrelated mortality worldwide, primarily due to poor tumor targeting, systemic toxicity, and drug resistance associated with conventional therapies. This review explores recent advances in nanoparticle-based drug delivery systems designed to address these challenges and improve therapeutic outcomes. Methods: A structured literature search was performed across PubMed, Scopus, ScienceDirect, and Web of Science for studies published between 2010 and 2024. Only peer-reviewed English-language articles were included, focusing on nanoparticle synthesis, characterization, targeting strategies, therapeutic applications, and clinical evaluations in lung cancer. Studies irrelevant to lung cancer or without experimental/clinical data were excluded. Data were thematically analyzed to identify innovations, therapeutic outcomes, and translational challenges. Results: Evidence indicates that diverse nanocarriers, including solid lipid nanoparticles, liposomes, polymeric nanoparticles, and metal-based systems, improve solubility, tumor penetration, and controlled drug release while reducing systemic toxicity. Preparation methods such as solvent evaporation, ionotropic gelation, and nanoprecipitation were frequently reported. Both passive targeting via the EPR effect and active targeting using ligands or antibodies enhanced site-specific drug accumulation. Integration of nanocarriers with immunotherapy, gene therapy, and photodynamic therapy demonstrated synergistic efficacy. Early-phase clinical trials of formulations such as liposomal cisplatin and docetaxel polymeric micelles suggest improved tolerability and tumor localization. Discussion: The integration of nanocarriers with therapies like immunotherapy, gene therapy, and photodynamic therapy has demonstrated enhanced antitumor efficacy and reduced side effects. Further, the development of multifunctional nanoparticles and theranostic systems has shown promise in personalizing treatment. Artificial intelligence is emerging as a powerful tool to optimize nanoparticle design and functionality. Conclusion: Nanoparticle-based delivery systems represent a transformative approach in lung cancer therapy by overcoming major limitations of conventional treatments. Future research should emphasize stimuli-responsive and AI-optimized nanocarriers to further enhance precision, personalization, and patient outcomes. Nanocarriers ; Drug ; Clinical trial ; Targeted drug delivery ; Drug delivery ; Docetaxel ; Tolerability ; Lung cancer Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Inhalation and Respiratory Drug Delivery
Authors: Shafi Ullah | Grace Victoria Poo | G. Yan | Shen Xin | Qiao Chunrui | Amna Aziz Khan | Qi Zhenhui
Source: Journal of Drug Delivery Science and Technology (article, 2026-01-08)
Field: Materials Science — Biomaterials

Nanocarrier-engineered therapeutics for Glioblastoma: Challenges and strategies for overcoming the blood-brain barrier Nanocarriers ; Translational research ; Drug delivery ; Novelty ; Biocompatible material ; Nanomedicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Huanbo Weng | Huang Yuan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Kolmogorov-Arnold Networks for Metal Fatigue: An Interpretable Approach to Fatigue Life Prediction Interpretability ; Extrapolation ; Artificial neural network ; Regression ; Symbolic regression ; Regression analysis ; Face (sociological concept) Machine Learning in Materials Science ; Machine Fault Diagnosis Techniques ; Fatigue and fracture mechanics
Authors: Peter Kepič | Petra Kalousková | Tomáš Šikola | Filip Ligmajer
Source: arXiv (Cornell University) (preprint, 2026-04-07)
Field: Materials Science — Polymers and Plastics

Controlled dewetting and phase transition hysteresis of VO2 nanostructures As artificial intelligence continues to grow, so does the need for more efficient ways to process data. Besides moving from electronic to photonic circuits, a promising approach is to integrate phase-change materials. Vanadium dioxide (VO$_2$) exhibits an ultrafast, near-room-temperature phase transition, characterized by hysteresis and large optical modulation -- making it a promising candidate for short-term memories and for mimicking neural behavior in brain-like computing systems. While the hysteresis behavior of VO$_2$ has been well studied in thin films and nanostructures, practical control and device integration have been limited only to thin films. Here, we demonstrate control over the phase transitions of VO$_2$ nanocylinders via lithographic patterning, controlled crystallization, and controlled dewetting. Because nanostructures are easier to address and consume less power than films, the ability to fabricate them with tailored geometry and hysteresis properties directly on integrated platforms is a key step toward scalable, energy-efficient memory and neuromorphic photonic devices. Dewetting ; Hysteresis ; Neuromorphic engineering ; Photonics ; Nanostructure ; Phase transition ; Thin film ; Phase (matter) ; Modulation (music) Transition Metal Oxide Nanomaterials ; Chemical and Physical Properties of Materials ; Phase-change materials and chalcogenides
Authors: Chuan He | Martin Singull | T. Jesper Jacobsson
Source: Advanced Intelligent Systems (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows Bayesian optimisation (BO) is a sample‐efficient strategy for optimising expensive, noisy, black‐box functions, challenges that are common in experimental sciences where each experiment may involve significant cost, time, and effort. Despite its theoretical strengths, BO remains under‐utilised in experimental disciplines, in part due to its perceived mathematical complexity and lack of practical guidance tailored to non‐specialists. This tutorial provides a comprehensive yet accessible introduction to BO, with an emphasis on implementation in chemistry, materials science, and related fields. We cover the core components of BO, including surrogate modelling with Gaussian Processes, acquisition functions, and sequential decision‐making. In addition, we discuss practical extensions for handling noise, constraints, multi‐objective, and high‐dimensional optimisation, mixed‐variable spaces, batch processing, and computational scalability. Strategies for incorporating domain knowledge and deploying BO in automated experimental workflows are also presented. Throughout, we provide practical advice, highlight useful software, and emphasise conceptual understanding over mathematical formalism. As laboratory automation and data‐driven discovery become increasingly central to modern science, we argue that BO will play a transformative role in accelerating intelligent experimentation. The goal of this paper is to lower the barrier to entry and equip the reader with the knowledge needed to apply BO effectively in their own work. Workflow ; Domain (mathematical analysis) ; Automation ; Bayesian probability ; Transformative learning ; Core (optical fiber) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Gaussian Processes and Bayesian Inference
Authors: Xiyao Wen | Zheng Wang | Tianyu Wang | Jialin Meng
Source: Advanced Functional Materials (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Brain‐Inspired Two‐Dimensional CuInP <sub>2</sub> S <sub>6</sub> Ferroelectric Materials for Neuromorphic Computing ABSTRACT 2D van der Waals (vdW) ferroelectric materials have emerged as transformative components in advancing neuromorphic computing and artificial intelligence applications. Among these novel vdW materials, copper indium thiophosphate (CIPS) has attracted significant attention due to its van der Waals layered structure and room‐temperature ultrathin ferroelectricity. It demonstrates great potential for fabricating novel CIPS‐based ferroelectric devices with unique crystalline structures and multifunctional characteristics, thereby promoting developments in non‐volatile multilevel memory, artificial synapses, and neuromorphic computing. Leveraging the interplay between robust ferroelectricity and ionic conductivity, CIPS‐based ferroelectric devices offer advantages such as ultra‐thinness, high endurance, strong retention, low power consumption, and multifunctionality. They show considerable application potential in artificial vision/auditory systems, image processing, bio‐sensory simulation, true random number generator, and logic gates. This review summarizes the classification of CIPS materials, device structures, and underlying mechanisms, while also discussing evaluation parameters for memory capability and synaptic plasticity in novel CIPS ferroelectric devices to enable efficient neuromorphic computing. It systematically outlines both hardware and software approaches for neuromorphic computing based on advanced CIPS devices, providing insights for future applications. Furthermore, the prospects and challenges in cutting‐edge application scenarios are thoroughly discussed, offering valuable guidance for the development of next‐generation neuromorphic computing devices. Neuromorphic engineering ; Ferroelectricity ; Memristor ; Non-volatile memory ; Artificial neural network ; Bioelectronics 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Stefan C. Soare | Seonghwan Choi | Tomáš Mánik | Myoung-Gyu Lee
Source: Materials & Design (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Non-associative flow in metal plasticity: Consistency and model development It is well known that polycrystal aggregates obeying Schmid law at grain level have an overall constitutive response defined by an associative flow rule (AFR), i.e., the rate of plastic deformation is along the outward normal of the yield surface. We show that when non-Schmid effects are accounted for, the overall response of the aggregate is non-associative. A minimal condition for the thermodynamic consistency of non-associative (NAFR) phenomenological models is that the gradient of the plastic potential be oriented towards the exterior of the elastic domain. The calibration of anisotropic NARF-models is currently done by fitting independently of each other the yield function and the plastic potential on yielding and deformation data. This increases the indeterminacy already observed for AFR-models, since data is never sufficient, particularly for sheet metal. We propose a rational approach to the calibration of NAFR-models based on the comparison with a best AFR-model. This involves the discovery of the appropriate range of mechanical data via an iterative procedure based on data-driven modeling and finite element simulation. We demonstrate our methodology by modeling the O and T4-tempers of the aluminum alloy AA6016 using hole expansion and cup drawing experiments reported in the literature. • Non-Schmid effects induce a split between the yield function and the plastic potential at macroscopic level (non-associative plasticity). • For thermodynamic consistency, the angle between the gradients of the yield function and of the plastic potential must be less than 90 . • The deviation of the yield function from the plastic potential can be estimated by solving an inverse problem using data-driven modeling and finite element simulations of complex deformation processes. • Results for the aluminum alloy AA6016 suggest that non-Schmid effects may be responsible for up to 20% of the observed thickness strains in stretching/drawing. Consistency (knowledge bases) ; Development (topology) ; Flow (mathematics) ; Metal ; Material flow Microstructure and mechanical properties ; High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming
Authors: Busheng Zhang | Mengmeng Li | Jian Huang | Hongming Chen | Weiguang Wang | Xinbo He
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Surface-Engineered Diamond/Liquid Metal Composites with Significantly Enhanced Thermal Conductivity for Advanced Thermal Interface Materials Thermal conductivity ; Thermal grease ; Wetting ; Diamond ; Electronics ; Thermal management of electronic devices and systems ; Composite number ; Thermal ; Computer cooling ; Liquid metal Thermal properties of materials ; Aluminum Alloys Composites Properties ; Aerogels and thermal insulation
Authors: Zahid Hussain | Rusel Mohammed Dhaidan | Ahmad Alshammari | Farida Ahmed Fikry | Hnin Ei Thu
Source: Journal of Drug Delivery Science and Technology (article, 2026-02-06)
Field: Materials Science — Biomaterials

Multimodal iron oxide nanoparticles for breast cancer theranostics and clinical translation: From bench to bedside Photothermal therapy ; Drug delivery ; Breast cancer ; Iron oxide nanoparticles ; Targeted drug delivery ; Cancer ; Bench to bedside ; Magnetic nanoparticles ; Cancer treatment Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Cancer Research and Treatments
Authors: Mahmoud Hamed | Reham E. Kannouma | Alaa Bedair | Fotouh R. Mansour
Source: Analytical Chemistry (article, 2026-03-23)
Field: Chemistry — Spectroscopy

Method-at-a-Glance (MAG) as a Graphical Approach for Method Development in Reversed-Phase Liquid Chromatography of Pharmaceuticals Reversed-phase high-performance liquid chromatography (RP-HPLC) remains the dominant separation mode in pharmaceutical and biomedical analysis, yet method development is still largely driven by trial-and-error practices, fragmented literature knowledge, or data-intensive chemometric tools. Despite decades of accumulated chromatographic experience, this knowledge is rarely transferred in a form that enables rapid, rational, and reproducible decision-making at the early stages of method development. This Perspective introduces Method-at-a-Glance (MAG), a conceptual, literature-encoded graphical framework that reimagines RP-HPLC method development as a visually guided process grounded in collective chromatographic knowledge. By abstracting and mapping reported chromatographic conditions for structurally related pharmaceutical classes, MAG enables the rapid identification of rational starting conditions, significantly reducing the scope of preliminary experimentation. Rather than proposing a new analytical method, this Perspective synthesizes the existing literature into a transferable visual intelligence tool and discusses its implications for accelerating method development, improving reproducibility, and reducing experimental burden. The broader opportunities of MAG are explored, including its potential role in AI-assisted chromatography and sustainability-oriented analytical design. MAG exemplifies how analytical chemistry can move beyond the isolated method reporting toward knowledge-integrated, concept-driven frameworks that support the evolving needs of modern laboratories. High-performance liquid chromatography ; Reversed-phase chromatography ; Quantitative analysis (chemistry) ; Separation method ; Qualitative analysis Analytical Chemistry and Chromatography ; Protein purification and stability ; Chromatography in Natural Products
Authors: Shaik Aminabee | Ogirala Umamaheswar Rao | Kunderu Ravi Shankar | Naga Venkata Chenchu Lakshmi Kollipara | Saritha Karnati | Atchutuni Venkata Subrahmanyeswara Ravi Sai Nadh
Source: Biosciences Biotechnology Research Asia (article, 2026-03-30)
Field: Materials Science — Biomaterials

Smart Nanobiotechnological Platforms for Gene and Drug Delivery: From Biopolymers to Pharmaceutical Applications Smart nanobiotechnological platforms have become a solace of revolution in conquering the challenges linked to the traditional drug and gene delivery system. The platforms allow targeted, stimuli-responsive, and precise delivery of therapeutic agents by incorporating the benefits of nanotechnology, biotechnology, and pharmaceutical sciences. The biocompatibility, biodegradability and versatility of biopolymers have been vital in designing smart nanocarriers since the biopolymers can be used to efficiently encapsulate drugs and genetic materials and release them on demand. The given review will be a complete overview of the application of smart nanobiotechnological platforms in gene and drug delivery with a particular emphasis on the key principles of the design, nanocarrier biopolymers, and stimuli responsiveness. They are particularly stressed in relation to their use in gene therapy, cancer therapy, neurology, and infectious diseases. The review further talks of pharmacokinetic and pharmacodynamic factors, safety and toxicity issues, and regulatory issues that arise with nanobiomaterials. The latest developments, such as the AI-assisted nanocarrier design and individualized nanomedicine are also discussed. In general, this review highlights the promise of smart nanobiotechnological platforms, which can transform the concept of delivering therapeutic interventions and speed up the process of translating nanomedicine into clinical care. Nanocarriers ; Nanomedicine ; Drug delivery ; Drug ; Gene delivery ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Advanced Drug Delivery Systems
Authors: Hongyu Pu | Mingyi Liu | Tingting Qiang | Pengbo Wang | Fanfei Ma | Jing Gan | Hao Chen
Source: Bioresource Technology (article, 2026-01-12)
Field: Materials Science — Biomaterials

Marine polysaccharide-based edible film as degradable alternative to plastic packaging: Preparations, applications and recent advances Commercialization ; Active packaging ; Food packaging ; Marine conservation ; Food products ; Biodegradation ; Plastic packaging Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution
Authors: Yiran Ma | Jerome Le Ny | Zhichao Chen | Zhihuan Song
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications. Uncertainty quantification ; Calibration ; Measure (data warehouse) ; Uncertainty analysis ; Measurement uncertainty ; Sampling (signal processing) ; Process (computing) ; Scalability Machine Learning in Materials Science ; Fault Detection and Control Systems ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Tingxuan Lu | Wen Zhu | Chenshi Lin | Zhenqing Zhang | Han LiNa | Jianzhou Cui
Source: Journal of Nanobiotechnology (article, 2026-01-23)
Field: Materials Science — Biomaterials

Sorafenib nanomedicine in HCC: nano-bio interactions and combination therapies Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with systemic therapies offering only limited benefit. Sorafenib, the first-line multi-kinase inhibitor, is constrained by poor pharmacokinetics, dose-limiting toxicities, and the rapid emergence of resistance. Nanotechnology-based drug delivery systems (NDDS) provide promising strategies to address these limitations. This review summarizes recent NDDS innovations that enhance sorafenib's therapeutic efficacy in HCC. Major nanocarrier classes, including lipid-based, polymeric, inorganic, and biogenic platforms, are discussed together with modifications for passive or active tumor targeting, such as asialoglycoprotein receptor-mediated approaches. Special attention is given to stimuli-responsive systems that exploit tumor microenvironmental cues to achieve localized drug release. Co-delivery strategies that combine sorafenib with chemotherapeutics, RNA interference agents, or immune modulators are also highlighted for their capacity to amplify antitumor activity and overcome resistance. Artificial intelligence (AI) is emerging as a tool to support the rational design and personalization of nanomedicines. While encouraging preclinical evidence demonstrates improved pharmacokinetics, tumor accumulation, and efficacy, challenges in scalable production, biosafety, and regulatory approval remain. Collectively, these developments emphasize the potential of sorafenib nanomedicines while underscoring the need to resolve key barriers for clinical translation. Sorafenib ; Nanomedicine ; Nanocarriers ; Hepatocellular carcinoma ; Drug delivery ; Drug ; Personalized medicine ; Drug discovery ; RNA interference Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Hepatocellular Carcinoma Treatment and Prognosis