jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Errika Voutyritsa | Athanasios Oikonomou | Georgios Bolis | Vasilis Papageorgiou | Aikaterini Stavroula Vougiatzi | Nikos S. Hatzakis
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-15)
Field: Materials Science — Biomaterials

Polymer functionalized liposomes as universal nanocarriers for drug delivery: Single particle insights on size-dependent performance and intracellular behavior Abstract Nanomedicine requires smart delivery systems that are precise, robust, and universal. While liposomes are established vehicles in drug delivery, their full potential is challenged by limited stability, leakage, insufficient response and limited insight into particle size dependent performance. Here, we provide polymer-modified liposomes (PMLs), engineered for high structural integrity, broad cargo compatibility, and stimuli-responsive cargo release. We thoroughly characterize PMLs at the single particle level shedding light on key structure-function relationships within polydisperse formulations revealing that small vesicles (&lt;100 nm) displayed significantly higher cargo packing densities, while release was independent of vesicle size. PMLs display high versatility effectively encapsulating cargo types ranging from positively or negatively charged small molecules, to oligonucleotides, and proteins. Studies on PMLs interaction with cell membrane show that PMLs maintain high internalization rate in HeLa and hCMEC/D3 brain cells and achieve ∼50% reduction in cell viability within 24 hours when loaded with the anticancer drug 5-fluorouracil. Finaly, PMLs successfully deliver siRNA targeting eGFP in HEK293-d2eGFP cells, achieving a 10-12% knockdown of eGFP expression, as resolved by machine learning-driven single-cell analysis. This work establishes a framework for PMLs high-resolution functional profiling and opens the way for the next generation rational design of tunable PMLs for drug delivery. Nanocarriers ; Liposome ; Drug delivery ; Particle size ; Materials science ; Nanotechnology ; Polymer ; Nanoparticle ; Targeted drug delivery ; Drug ; Chemical engineering ; Pharmacology ; Medicine ; Composite material ; Engineering Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Lipid Membrane Structure and Behavior
Authors: Pranesh RK | N. Mani | Divya U | Mengasoundari B
Source:  (article, 2025-11-06)
Field: Materials Science — Biomaterials

Design of Hydrosentinel for Matsya Using Image Processing Technique The HydroSentinal is an innovative fish health monitoring system designed to enhance aquaculture management through advanced image processing techniques. Specifically developed for Matsya (fish) applications, the system captures high-resolution underwater images of fish gills and body features using corrosion-resistant, camera-integrated platforms such as RC boats. Captured images are processed using color-based segmentation, edge detection, and machine learning algorithms to detect abnormalities, including inflammation, discoloration, or parasitic infections. By providing real-time health assessments, the HydroSentinal enables early intervention, reducing fish mortality and improving yield.In addition to health monitoring, the HydroSentinal can be integrated with water quality sensors to measure parameters such as temperature, pH, and turbidity, creating a comprehensive aquatic ecosystem assessment tool. The device’s portability and corrosion-resistant materials make it well-suited for deployment in rural and village settings.The system’s data analytics capabilities allow trends to be tracked over time, enabling farmers and aquaculture managers to make informed decisions regarding stocking density, feed schedules, and disease control measures. By reducing reliance on manual inspections and costly laboratory tests, HydroSentinel promotes cost-effective and sustainable aquaculture practices. Its modular design further allows future integration of autonomous navigation, wireless data transfer, and cloud-based dashboards for remote monitoring.The HydroSentinel thus represents a scalable, field-ready innovation that combines mechanical design, embedded systems, and computer vision to improve fish welfare, optimize resources, and support sustainable aquaculture development in diverse water environments. Aquaculture ; Software portability ; Modular design ; Software deployment ; Sustainable development ; Image processing ; Data processing ; Machine vision Advanced Cellulose Research Studies ; Dyeing and Modifying Textile Fibers ; Synthesis and properties of polymers
Authors: Sebastian Leal | Pablo Gómez | Hans García | Henry Argüello
Source:  (article, 2025-08-27)
Field: Chemistry — Analytical Chemistry

End-to-End Optimized DOE for Spatial Classification in Agricultural Imaging Precision agriculture aims to optimize crop production using advanced sensing and computational methods. Traditional RGB imaging systems often fail to capture the subtle spectral differences necessary for accurate classification of materials in agricultural scenes, as many crop and soil properties exhibit unique spectral signatures in the visible range. This paper introduces a compact, static optical system based on a diffractive optical element (DOE), jointly optimized with a spatial classification model through an end-to-end (E2E) learning framework. The goal is to enhance spatial classification performance without increasing system complexity. This work models light propagation using angular spectrum theory, employ Zernike polynomial parameterization to constrain the DOE design space, and integrate a neural network trained on CALCROP21 data. Simulations demonstrate that the proposed E2E-optimized DOE outperforms baseline RGB-based systems in classification accuracy while maintaining high light efficiency and enabling offline processing. These findings highlight the potential of task-specific DOE design for embedded agricultural imaging systems, achieving an accuracy increase in the best cases up to 20.9% compared to conventional RGB-based systems. RGB color model ; Zernike polynomials ; Precision agriculture ; Artificial neural network ; Baseline (sea) ; Pattern recognition (psychology) ; Design of experiments Spectroscopy and Chemometric Analyses
Authors: Ángel Yanguas-Gil
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> AI for ALD: Accelerating Process Development, Materials Discovery, and Scale up Fast process optimization is critical to help reduce the cost of development and adoption of new ALD processes. Two examples are energy technologies, where low cost manufacturing is key to ensure commercial viability, and microelectronics, where the cost of innovation is very high due to a combination of substrate complexity, sometimes requiring many prior processing steps, and ever stringent requirements in terms of conformality, roughness, and precision. In this presentation I will provide an overview of how machine learning and AI can help accelerate process optimization and materials discovery in atomic layer deposition. In particular, I will highlight three different approaches: the first one explores the use of surrogate models to connect experimental metrology data with optimal processing conditions. We have explored two different cases: optimizing a process within a reactor and optimizing process transfer to a different reactor. In both cases, we show that, for thermal ALD processes, the information contained in thickness profiles in undersaturated conditions is enough to help predict optimal dose times both within and across different reactors. We also extended this methodology to the case of plasma-assisted deposition processes. The second approach relies on the use of in-situ characterization techniques to design self-driving deposition tools that can automatically search and identify optimal process conditions. For this approach, we developed a two-step process where algorithms are tested first using simulations and digital twins of the reactors before being experimentally deployed. This methodology can lead to x100 faster process optimization compared to standard growth-vent-characterize optimization cycles. We are currently applying this approach to the growth of hybrid organic/inorganic materials using molecular layer deposition. Finally, I will focus on large language models and their potential to assist in ALD research, both in terms of their role as research assistants and in the design of experiments. For the former case, have we developed a benchmark focused on ALD that we have applied to GPT-4o model. For the latter, I will describe how you can integrate LLMs with experimental ALD tools. This research is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. Process (computing) ; Process optimization ; Scale (ratio) ; Metrology ; Key (lock) ; Process modeling ; Process development ; Digital manufacturing Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Raj Kumar Gupta | Vaishali B. Langote | Sundar Raj. M | Anu Tonk | Nanthakumar.S | Emmela suneetha | Mayakannan Selvaraju
Source: Journal of Environmental Nanotechnology (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Performance Evaluation and MLP Prediction of Flax and Graphene Oxide Reinforced Polyester Composites. This research examines the mechanical properties of composites made from flax fiber and polyester resin. Hybrid composites were produced by integrating flax fiber, known for its exceptional tensile and flexural strengths, with graphene oxide nanoparticles (nGO) and polyester resin. The flexural and ultimate tensile strengths of the composites were predicted using a multilayer perceptron (MLP) regression model, a type of artificial neural network (ANN). The composite material FGO-1, comprising 40 wt.% flax fiber, 1 wt.% graphene oxide nanoparticles, and 59 wt.% polyester resin, exhibited outstanding mechanical properties. The FGO-1 sample showed improvements in flexural modulus (46.80%), flexural strength (50.80%), tensile modulus (32%), and tensile strength (37.83%). The energy absorption rate was 32.69%, and its impact resistance exceeded that of the other composite formulations by 8.75%. The ANN model successfully predicted these strengths, achieving R2 values of 0.89 and 0.98 for flexural and tensile strengths, respectively. Microstructural analysis using a scanning electron microscope (SEM) was conducted alongside tensile, flexural, impact, and interlaminar shear strength tests to evaluate the composite’s performance. Flexural strength ; Ultimate tensile strength ; Flexural modulus ; Graphene ; Polyester ; Young's modulus ; Composite number Natural Fiber Reinforced Composites ; Scientific and Engineering Research Topics ; Aluminum Alloys Composites Properties
Authors: Hongbin Song | Yingying Guo | Chenxi Cao | Suwei Jin | Shanshan Zhang | Ruile Pan | Yunfeng Zhou | Mingzhu Yan | Qi Chang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Multi-regional HPLC fingerprinting of Nelumbinis stamen flavonoids in China and machine learning-guided analysis of environmental drivers Stamen ; Flavonoid ; High-performance liquid chromatography ; Similarity (geometry) ; Chemical constituents Chromatography in Natural Products ; Traditional Chinese Medicine Analysis ; Phytochemical Studies and Bioactivities
Authors: Yuxing Ma | Yingwei Chen | Binhua Zhang | Yali Yang | Hongyu Yu | Xin-Gao Gong | Yang Zhong | Hongjun Xiang
Source: Physical review. B./Physical review. B (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Prediction of local electric polarization using graph neural networks The calculation of electric polarization based on the Berry phase (BP) method plays a crucial role in understanding ferroelectric materials. However, it cannot capture local polarization within the supercell, especially in systems with complex topological structures such as flexoelectric structures and polar skyrmions. In this study, we present a method for calculating flexoelectric coefficients via graph neural networks (GNNs). We represent the polarization contribution of each unit cell to the overall supercell as the sum of the tensor contributions from each atom, while the tensor contribution from each atom can be represented as a linear combination of the local spatial components projected along the edge directions of a cluster centered on that atom. The local polarization can be obtained using GNNs without density-functional theory calculations. Using this method, we accurately calculate the flexoelectric coefficients in $\mathrm{BaTi}{\mathrm{O}}_{3}$ and Si, as well as the polarization distribution in the $\mathrm{PbTi}{\mathrm{O}}_{3}/\mathrm{SrTi}{\mathrm{O}}_{3}$ superlattice. Our method provides a practical approach for local contributions of electric polarization calculations in large-scale systems. Artificial neural network ; Computer science ; Polarization (electrochemistry) ; Graph ; Artificial intelligence ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Neural Networks and Applications ; Solid-state spectroscopy and crystallography
Authors: Rong Qiu | Qiyu Zeng | Bo Chen | Jinsen Han | Jiahao Chen | Qunchao Tong | Kaiguo Chen | Dongdong Kang | Xiaoxiang Yu | Han Wang | Jiayu Dai
Source: Physical Review Letters (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Complex Phonon Behaviors Dictate Anisotropic and Nonmonotonic Thermal Transport in Ice Polymorphs The thermal conductivity of ice polymorphs constitutes a critical parameter in multidisciplinary research spanning cryobiology, atmospheric physics, and planetary science. However, the intricate structures and phonon dynamics pose significant challenges to understanding thermal transport in ice polymorphs across diverse phase diagrams. To bridge this knowledge gap, we combined deep learning potential with molecular dynamics simulations to investigate the thermal transport in ice polymorphs with ab initio accuracy across 150-400 K and 10^{-3}-10 GPa. Pronounced anisotropic behaviors and nonmonotonic pressure dependence of thermal transport are demonstrated. Phonon dynamics analysis reveals the structure-phonon relationship and rich phonon behaviors, including nonuniformity-induced phonon hybridization, distortion-induced phonon softening, and disorder-induced phonon scatterings. Configurational local entropy quantification identifies the complexity of the hydrogen bond network as the governing factor in thermal transport variation. Our Letter provides fundamental insights into microscopic energy transport in complex hydrogen bond systems. Phonon ; Thermal conductivity ; Anisotropy ; Molecular dynamics ; Thermal ; Ab initio ; Energy landscape Thermal properties of materials ; Material Dynamics and Properties ; High-pressure geophysics and materials
Authors: Qize Zhong | Menghan Xiong | Pingsheng Zhang | Yuanpeng Si | Fangcheng Cao | Zhikai Gan | Xingyan Zhao | Yang Qiu | Shaonan Zheng | Yuan Dong | Jiamu Lin | Chun Lin | Ting Hu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

An All-silicon Metalens for Imaging and Recognition in the MIR Optical transfer function ; Distortion (music) ; Limit (mathematics) ; Modulation (music) ; Phase modulation ; Diffraction ; Phase (matter) ; Inverse problem ; Convolutional neural network ; Function (biology) Transition Metal Oxide Nanomaterials ; Metamaterials and Metasurfaces Applications ; Advanced Semiconductor Detectors and Materials
Authors: Rahman, Md Naimur
Source: DiVA (Mälardalen University College) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-BASED SEGMENTATION OF INTERMITTENT FAULTS AND PARTIAL DISCHARGES IN POWER SYSTEMS High-voltage electrical systems are the backbone of modern industry and infrastructure, but they remain vulnerable to insulation failures that can cause severe operational and financial risks. Partial discharges (PD) are among the earliest indicators of such failures, appearing as transient electrical events that signal stress within insulation materials. Detecting and precisely locating these events is crucial for ensuring the reliability and safety of power systems. This thesis focuses on the challenge of accurately identifying the temporal boundaries of PD activity in high-frequency electrical signals, a task often complicated by noise and variability. To address this, we propose a segmentation framework built on a customized U-Net architecture enhanced with attention mechanisms, allowing the model to capture both local and global signal patterns. Combined with traditional signal processing techniques for filtering out irrelevant data, this deep learning approach isolates the onset and offset of PD events with high precision. By improving the accuracy of event boundary detection, our work supports more effective maintenance planning, minimizes unnecessary inspections, and enables condition-based asset management in high-voltage power grids. Segmentation ; Offset (computer science) ; Electric power system ; Reliability (semiconductor) ; Signal processing ; SIGNAL (programming language) ; Electric power industry ; Condition monitoring ; Noise (video) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Xinlei Qian | Ying Ji | Xiaolu Ge | Yue Huang | Chongpeng Huang | Yong Kong
Source: Optics Communications (article, 2025-03-18)
Field: Chemistry — Analytical Chemistry

Prediction of humidity levels for maximum optical relative transmittance using modified heterodyne detection and GA-BP neural networks Transmittance ; Optics ; Relative humidity ; Heterodyne (poetry) ; Heterodyne detection ; Materials science ; Artificial neural network ; Physics ; Computer science ; Laser ; Acoustics ; Meteorology ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Fiber Optic Sensors ; Semiconductor Lasers and Optical Devices
Authors: S. Jayalelshmi | James J. Farrell | Y. Chen | Marlén Izquierdo | Sergey Makarychev‐Mikhailov
Source: SPE International Conference on Oilfield Chemistry (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Can Artificial Intelligence Design Your Experiment? A Carbon Capture Example Abstract In this paper, we introduce a sequential design of experiments (SDOE) methodology tailored for laboratory research, with a specific focus on novel chemistry for carbon capture. By intelligently guiding the selection of experiments, SDOE iteratively enhances data model performance while reducing the number of required experiments by up to 50%, ensuring more efficient and impactful outcomes. The proposed SDOE methodology is based on modern active learning principles, offering an adaptive and iterative approach to experimentation. It dynamically adjusts upcoming experiments and accommodates major changes to the experimental program, such as introducing new features, parameters, or refining feature ranges. At the core of this methodology is a machine learning (ML) model that is automatically retrained after each experiment. Using various active learning strategies (acquisition functions), including exploration, exploitation, and hybrid approaches, SDOE identifies the next experiment with the highest expected information gain. The novel carbon capture system exhibits highly variable physico-chemical properties across different compositions and conditions. In the SDOE workflow, we selected Gaussian process regression (GPR) as a robust ML algorithm for predicting physical properties (the direct problem) and CO2 concentration (the inverse problem). Starting with a small initial training dataset, the SDOE process trains an initial model to predict responses across a pool of potential experiments. Active learning strategies then guide the selection of subsequent experiments based on various criteria such as uncertainty (for exploitation), diversity (for exploration), or hybrid methods balancing uncertainty and diversity. Each completed experiment adds new data to the training set, allowing the model to be iteratively updated. This iterative approach significantly reduces the number of experiments required to develop accurate predictive models, cutting time, cost, and resource utilization while maximizing experimental efficiency. Overall, this SDOE methodology accelerates the novel carbon capture technology optimization by integrating adaptive, AI-driven strategies into laboratory programs. Its flexibility allows for real-time adjustments and resource efficiency. Having successfully developed and demonstrated SDOE for this application, we aim to extend this active-learning-based approach to other experimental fields, promising substantial economic and scientific benefits. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Scientific Computing and Data Management ; Carbon Dioxide Capture Technologies
Authors: William Wen | Jordan Clive Barker | Yun Wang
Source: AI for Materials (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Supervised Machine Learning Assisted Development of Hybrid Solvation Model for Simulating Graphene-Water Interface The electrified graphene-water interface is a vital component in many energy storage applications. However, understanding the interfacial properties is challenging due to the requirement of a high-quality atomic interfacial model. Recently, the hybrid solvation model, including the computationally affordable implicit solvation model and a thin layer of explicit water solvent slab next to the solid, has become a promising approach to address this issue. The identification of the rational explicit water slab thickness holds the key to the computational results by using this hybrid solvation model. In this study, we present a framework combining ab initio molecular dynamics (AIMD) and supervised machine learning (ML) to address this challenge. Based on the database from the AIMD simulations, the relationship between the total energy of the system and the distance from the oxygen in water molecules to the graphene was successfully identified through supervised ML. Our results further demonstrate that the first few layers of water next to the graphene play the decisive role in the change of the total energy. The cutoff thickness of 7 Å can reproduce the majority of the impact of the solvent on the total energy change of the water-graphene system. The success of this ML-assisted platform suggests it can also be used as a protocol to build the hybrid solvation model for understanding other electrified solid-liquid interfaces. Solvation ; Interface (matter) ; Artificial neural network ; Graphene ; Energy (signal processing) ; Component (thermodynamics) ; Representation (politics) ; Ab initio ; Molecular descriptor ; Implicit solvation Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Electrocatalysts for Energy Conversion
Authors: Xiaolin Ran | Shaojiang Wang | Haoyu Li | Yunlong Wang | Huiru Li | Jiayu Xie | Wenfeng Qin | Rui Zhong | Chung‐Yuan Mou
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Organic Chemistry

Bamboo cellulose fiber/Aramid nanofiber/MXene porous composite membrane pressure sensor with wide sensing range and long-term stability for motion monitoring and handwriting recognition Pressure sensor ; Piezoresistive effect ; Convolutional neural network ; Wearable computer ; Filtration (mathematics) ; Sensitivity (control systems) ; Membrane ; Wireless sensor network ; Composite number Polydiacetylene-based materials and applications ; Advanced Sensor and Energy Harvesting Materials ; IoT-based Smart Home Systems