jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongqi Fan | Xu Chen | Shan Wang | Jun Zhan | Yuan Chen | Houqi Zhou | Dize Li | Han Tang | Qingqing He | Tao Chen
Source: Angewandte Chemie International Edition (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Prediction of Photothermal Metal‐Phenolic Networks Photothermal therapy (PTT) demonstrates significant potential in cancer treatment, wound healing, and antibacterial therapy, with its efficacy largely depending on the performance of photothermal agents (PTAs). Metal-phenolic network (MPN) materials are ideal PTA candidates due to their low cost, good biocompatibility and excellent ligand-to-metal charge transfer properties. However, not all MPNs exhibit significant photothermal properties, and the vast chemical space of MPNs (over 700,000 potential combinations) complicates the screening of high-photothermal materials. To address this challenge, this study introduces machine learning (ML) methods for predicting the photothermal performance of MPNs. A database of photothermal properties of 80 modular MPNs was constructed, and the ML process was optimized through feature engineering and model training. The selected extreme gradient boosting model (XGBoost) successfully identified 1,654 high photothermal MPNs from a virtual database of 44,438. Subsequent experimental validation revealed a remarkable success rate of 70 % in predicting high photothermal MPNs. Additionally, several previously unreported high photothermal MPNs were discovered, demonstrating advantages in photothermal antibacterial applications. This study offers an innovative ML-driven approach for the efficient screening of MPN materials, providing a solid foundation for PTA design in PTT and other biomedical applications. Photothermal therapy ; Biocompatibility ; Materials science ; Photothermal effect ; Nanotechnology ; Machine learning ; Computer science Machine Learning in Materials Science ; Nanoplatforms for cancer theranostics ; Polydiacetylene-based materials and applications
Authors: Bakhtawar Shafique | Zuyao Fu | Hao Wang | Jing Xie | Zhaoyang Ding
Source: Food Control (article, 2025-06-15)
Field: Materials Science — Materials Chemistry

Multifunctional metal-organic framework based nanozymes: From synthesis to fluorescent and colorimetric dual mode sensing in food safety Dual mode ; Fluorescence ; Dual (grammatical number) ; Food safety ; Metal-organic framework ; Metal ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Organic chemistry ; Food science ; Physics ; Engineering ; Electronic engineering ; Optics Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Giuseppe Bonifazi | Giuseppe Capobianco | Paola Cucuzza | Silvia Serranti
Source: Waste Management (article, 2025-02-10)
Field: Chemistry — Analytical Chemistry

Contaminant detection in flexible polypropylene packaging waste using hyperspectral imaging and machine learning Flexible plastic packaging (FPP) constitutes one of the largest post-consumer plastic streams processed in recycling facilities. To address the key challenges of its sorting and quality control, this study developed and tested a classification procedure based on hyperspectral imaging (HSI), combined with machine learning. The aim was to automatically detect contaminants (i.e., other polymers and materials) within a polypropylene (PP) stream of FPP waste (FPPW). Hyperspectral images of representative FPPW samples of PP and contaminants were acquired in the short-wave infrared range (SWIR: 1000-2500 nm) and preprocessed using different combinations of algorithms to emphasize their spectral characteristics. Principal component analysis (PCA) was applied as exploratory analysis of the spectral data followed by the application of a hierarchical classification model, based on partial least squares-discriminant analysis (Hi-PLS-DA), to differentiate between PP and other materials considered as contaminants, including polyethylene, polyester, polyethylene terephthalate, polystyrene, cellulose, polyurethane, aluminum and multilayer films. The results showed a classification accuracy of 87.5 %, with 147 out of 168 flakes correctly identified, as verified by Fourier transform-infrared (FT-IR) spectroscopy, demonstrating the model robust performance in distinguishing PP from other materials. Assuming all correctly identified particles are properly sorted, the model is predicted to achieve a Recovery of 98.2 % by weight for PP, indicating minimal material losses, with a Grade of 94.4 % by weight, representing a significant improvement compared to 77.2 % in the initial feed FPPW stream. This work demonstrated the effectiveness of HSI combined with Hi-PLS-DA in developing an automatic and efficient sorting and/or quality control process for FPPW, with minor classification errors occurring in filaments and multilayer flakes. Hyperspectral imaging ; Polypropylene ; Waste management ; Environmental science ; Materials science ; Process engineering ; Engineering ; Computer science ; Artificial intelligence ; Composite material Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Industrial Vision Systems and Defect Detection
Authors: Alin M. Elena | P.S. Kamath | Théo Jaffrelot Inizan | Andrew Rosen | Federica Zanca | Kristin A. Persson
Source: npj Computational Materials (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Machine learned potential for high-throughput phonon calculations of metal—organic frameworks Abstract Metal–organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics. Throughput ; Phonon ; Metal-organic framework ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Physics ; Condensed matter physics ; Physical chemistry ; Telecommunications Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Mikołaj Feculak | Susana Loureiro | Jason C. White | Baoshan Xing | Kevin C.‐W. Wu | Mohamed S. Sheteiwy | Yanzheng Gao | Patryk Oleszczuk | Izabela Jośko
Source: Nano Today (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Engineered nanoparticle transformations: Rethinking toxicity in water The burgeoning production and utilization of engineered nanoparticles (ENPs) in recent years has precipitated the intentional and inadvertent discharge of ENPs into the environment, where undergo different transformations. Extensive research has investigated the mechanisms underlying the environmental transformations of metal-based ENPs, with a focus on alterations in the properties of their transformation products. It is widely recognized that ENP-biota interactions are influenced by various ENP characteristics, such as size, shape, surface area, chemical composition, surface charge, and chemistry. As a result of transformations, changes in ENP properties are anticipated to affect biotic interactions, including cellular recognition and trafficking, thus impacting organismal responses. This hypothesis has only recently been subjected to experimental scrutiny, mainly within simplified ENP-organism systems. Major studies indicate that the acute toxicity of transformed ENPs is largely driven by the rate and yield of metal ion release, similar to pristine ENPs. However, when transformations reduce ENP dissolution, they may enhance environmental persistence, rendering other toxicity mechanisms more significant. We meticulously examine available data on the toxicity of various transformed ENPs, aiming to systematically assess the actual responses of aquatic biota concerning altered ENP properties and differing environmental factors. In this context, we highlight scenarios involving multiple ENP transformations and specific local environmental modifications. These research directions warrant further exploration, especially under real-world conditions. Such efforts will expand the database, which, through the application of modern machine learning and artificial intelligence tools, can aid in predicting the fate of ENPs released from the increasing array of nano-products. • ENPs transformations alter material properties and fate in the environment. • Environmental conditions greatly influence the type and course of ENP transformations. • Organism microenvironments create specific conditions for ENPs transformations. • Biotransformation of ENPs primarily mitigates their acute toxicity to aquatic biota. • Reduced dissolution, direct contact, and ROS levels govern lower toxicity of transformed ENPs. Nanoparticle ; Nanotechnology ; Toxicity ; Chemistry ; Materials science ; Organic chemistry Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanomaterials for catalytic reactions
Authors: Thi Huyen Chau Nguyen | Jeongho Lee | Dawoon Lee | Manh Cuong Nguyen | Jaekyun Kim
Source: Chemical Engineering Journal (article, 2025-01-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Omni-directionally flexible, high performance all-solid-state micro-supercapacitor array-based energy storage system for wearable electronics Supercapacitor ; Electronics ; Solid-state ; Energy storage ; Wearable computer ; Wearable technology ; Materials science ; Nanotechnology ; Electrical engineering ; Computer science ; Embedded system ; Engineering ; Capacitance ; Electrode ; Engineering physics ; Chemistry ; Physics Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Pei-Te Lin | Zi-Chun Tseng | Chun‐Ying Huang
Source: ACS Applied Electronic Materials (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Assisted Wavelength Recognition in Cu<sub>2</sub>O/Si Self-Powered Photodetector Arrays for Advanced Image Sensing Applications The ability of a photodetector array (PDA) to detect multiple wavelengths significantly expands its range of potential applications. However, effectively detecting and distinguishing between different wavelength bands remains a challenge for these arrays. This study introduces an approach for wavelength recognition in PDAs by integrating machine learning techniques with solution-processed Cu2O/Si heterojunction photodetectors. We propose a simple solution-processing method to fabricate a PDA consisting of a 4 × 4 array of p-Cu2O/n-Si photodiodes. This method involves low-power UV irradiation of a molecular precursor film containing Cu (II) complexes to produce a p-type Cu2O thin film on a Si substrate. A UV-shielding glass plate is used as a patterning mask, and water is used to wash away the UV-shielded areas. Using machine learning techniques, we effectively classify various wavelengths of light, including UV, visible, and near-infrared, and accurately predict their corresponding photocurrents in the Cu2O/Si heterojunction. Notably, the PDA enables clear identification of images across different light wavelengths. This PDA paves the way for advanced applications in multispectral imaging and sensing technologies. Photodetector ; Optoelectronics ; Materials science ; Multispectral image ; Wavelength ; Photodiode ; Heterojunction ; Image sensor ; Optics ; Substrate (aquarium) ; Computer science ; Artificial intelligence ; Physics Ga2O3 and related materials ; ZnO doping and properties ; Transition Metal Oxide Nanomaterials
Authors: Efi-Maria Papia | Alex Kondi | Vassilios Constantoudis
Source: Microporous and Mesoporous Materials (review, 2025-05-05)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning applications in SEM-based pore analysis: a review Scanning Electron Microscopy (SEM) is a cornerstone technique for analyzing porous materials, providing high-resolution images essential for understanding material properties and performance. However, traditional SEM image analysis methods often involve manual interpretation and are limited by challenges such as noise, segmentation difficulties, and resolution constraints. Recent advancements in machine learning have revolutionized SEM image analysis, offering automated, accurate, and scalable solutions. These technologies enable precise pore size distribution measurement, pore shape classification, and network connectivity analysis while enhancing image quality through advanced denoising techniques. This paper reviews the integration of Artificial Intelligence (AI) in SEM-based porous material analysis, discussing its applications, challenges, and future directions. Through highlighting key contributions in the field, we aim to provide a comprehensive overview of how AI is reshaping SEM image analysis and unlocking new possibilities for porous material characterization, also emphasizing challenges and limitations that arise. • AI enhances SEM analysis efficiency for porous materials. • We review current progress in AI for SEM-based porous material analysis. • Applications include pore segmentation and pore function related analysis. • SEM image denoising and pore simulation/reconstruction are also explored. • Key challenges in SEM imaging and AI applications for porous materials are discussed. Materials science ; Chemical engineering ; Nanotechnology ; Computer science ; Engineering Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies
Authors: L. Natrayan | Naga Dheeraj Kumar Reddy Chukka | S. Kaliappan | V. Balaji | Nimel Sworna Ross | Ramya Maranan
Source: Results in Engineering (article, 2025-04-01)
Field: Materials Science — Polymers and Plastics

An integrated Artificial neural network technique to optimize the various parameters of Pineapple/SiO2/epoxy-based nanocomposites under NaOH treatment • Optimized the mechanical properties of pineapple/SiO₂/epoxy nanocomposites using artificial neural networks (ANN) and Taguchi methods, focusing on tensile, flexural, and impact strength. • Introduced ANN for precise parameter optimization in natural fiber composites, achieving a prediction accuracy with R 2 >0.97, a first in PALF/SiO₂/epoxy studies. • Optimal configuration (20 % fiber content, 10 mm fiber length, 5 wt % SiO₂, 6 h NaOH treatment) provided significant enhancements: tensile strength (45.01 MPa), flexural strength (67.23 MPa), and impact resistance (34.51 kJ/m²). • Utilized eco-friendly pineapple leaf fibers and chemical treatments to produce high-strength, lightweight composites with minimal environmental impact. • Developed composites are ideal for automotive interiors, aerospace components, and structural applications requiring lightweight and high-strength materials. Natural fibre and nano-filler-based composites are gaining popularity because of their high strength, recyclable nature, high strength-to-weight ratio, and other benefits. The primary goal of this study is to maximize the many factors that influence the mechanical characteristics of hybrid nanocomposites. The nanocomposites were created using the standard hand lay-up method with the following conditions: (i) fibre content, (ii) length of pineapple fibre in mm, (ii) weight proportion of nano SiO 2 ; and (iii) alkali treatment periods. Following manufacture, the composite's mechanical characteristics were tested according to ASTM standards. Quantitative optimisation using the Taguchi technique Depending on the S/N ratio of Taguchi refinement, the nanocomposite with the maximum mechanical performance contains 20 % pineapple fibre with a length of 10 mm, 5 % nano SiO 2 , and 6 h of NaOH treatment. The ANN predictive algorithm and the Taguchi L 27 array show that the experiment and projected data for tensile, flexural, and impact are within 3 % and 4 %. According to the ANOVA study, the NaOH approach provides around 45 %, followed by the pineapple fibre content of 25 %, fibre length of 20 %, and filler content of 5 %. The similarity between artificial neural networks and test findings expands the scope of ANN for forecasting strength properties. This approach will assist designers in predicting system failures concerning the operating duration and may be utilised in numerous industrial industries to analyse toughness attributes. Epoxy ; Nanocomposite ; Artificial neural network ; Materials science ; Composite material ; Process engineering ; Computer science ; Engineering ; Artificial intelligence Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Polymer Nanocomposites and Properties
Authors: Cong Cheng | Mengxue Li | Chenyu Shi | Shiyu Hu | Min Chen | Yongbing Cheng | Qinyi Cao | Xiaoli Wang | Xianghong Kong | Zihao He | Guiping Liao | Zhaohong Su
Source: Chemical Engineering Journal (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Hierarchical all-carbon nanozyme architectures for enhanced intelligent rutin detection Rutin ; Carbon fibers ; Chemistry ; Nanotechnology ; Computer science ; Materials science ; Organic chemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Nanocluster Synthesis and Applications
Authors: Ena Dražić | Darijan Jelušić | Patrizia Janković | Goran Mauša | Daniela Kalafatović
Source: ACS Nano (review, 2025-05-29)
Field: Materials Science — Biomaterials

Using Machine Learning to Fast-Track Peptide Nanomaterial Discovery Peptides can serve as building blocks for supramolecular materials because of their unique ability to self-assemble, offering potential applications in drug delivery, tissue engineering, and nanotechnology. In this review, we describe peptide self-assembly as a sequence- and context-dependent process and its resulting complexity due to the heterogeneity of the sequences and experimental conditions, which makes cross-laboratory reproducibility a serious challenge and standardized reporting a necessity. Given the large number of possible peptide permutations, machine learning (ML) is suitable for navigating the peptide search space with the aim of reducing trial-and-error experimentation and speeding up the discovery of self-assembling peptides. However, we point out that ML is not a point-and-shoot tool that can be applied directly to any problem and requires careful consideration, domain knowledge, and proper data preparation to achieve meaningful results. In addition, we discuss the lack of negative data reported to be the main limiting factor in the effective application of ML. Considering the transformative potential of artificial intelligence, we conclude that grasping the power of large language models and generative approaches, coupled with explainability techniques, will expedite peptide nanomaterials discovery. Track (disk drive) ; Nanomaterials ; Nanotechnology ; Peptide ; Computer science ; Artificial intelligence ; Materials science ; Chemistry ; Biochemistry Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Advanced biosensing and bioanalysis techniques
Authors: Jing Li | Huanyang Chen | S. L. Yap | Binzhen Zhang
Source: Optics & Laser Technology (article, 2025-02-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A temperature-insensitive graphene-water-based ultra-wideband terahertz metamaterials absorber designed using deep neural networks To address the limitations of traditional wave-absorbing materials in electromagnetic wave absorption, this study utilizes the electromagnetic properties of water and graphene in the terahertz (THz) band, coupled with deep neural networks (DNN), to propose a temperature-insensitive, ultra-wideband (UWB) THz metamaterials absorber (MAs). Simulation results show that when the graphene Fermi level is E f = 0.9 eV, the absorber achieves an absorption rate exceeding 90 % over the 3.832 ∼ 9 THz frequency range. Analysis of the water-graphene composite structure reveals that the ultra-wideband absorption is primarily attributed to the coupling between the top-layer graphene and the dielectric water layer. A comprehensive investigation of the absorption mechanism is carried out using transmission line theory , impedance matching theory, and the analysis of field distribution and power loss. Moreover, the study demonstrates that adjusting the graphene Fermi level (0.01 ∼ 0.9 eV) enables flexible tuning of the absorber’s bandwidth and absorption performance . Additionally, the absorber remains stable across a temperature range of 0 ∼ 100 °C and exhibits wide-angle and polarization-insensitive absorption characteristics. With its simple structure, compact size, superior absorption performance, and tunability, this absorber shows great potential for applications in THz thermal imaging, radar stealth, smart switches, and electromagnetic radiation protection. Terahertz radiation ; Metamaterial ; Graphene ; Materials science ; Wideband ; Optoelectronics ; Metamaterial absorber ; Terahertz metamaterials ; Tunable metamaterials ; Optics ; Nanotechnology ; Physics ; Far-infrared laser Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Peihua Zhang | Rifeng Jiang | Boyuan Li | Aoxun Liang | Xueye Chen | Xiaoxiao Chu
Source: Chemical Engineering Journal (article, 2025-02-26)
Field: Materials Science — Surfaces, Coatings and Films

Fabrication and energy collection of PDMS/dimethylsilicone oil superhydrophobic high tensile film Fabrication ; Materials science ; Ultimate tensile strength ; Composite material ; Nanotechnology Surface Modification and Superhydrophobicity ; Fluid Dynamics and Thin Films ; Advanced Sensor and Energy Harvesting Materials
Authors: Aqib Iqbal Dar | Shiwani Randhawa | Mohini Verma | Trilok Chand Saini | Amitabha Acharya
Source: Advances in Colloid and Interface Science (review, 2025-05-01)
Field: Materials Science — Biomaterials

Debugging the dynamics of protein corona: Formation, composition, challenges, and applications at the nano-bio interface Interface (matter) ; Nano- ; Corona (planetary geology) ; Nanotechnology ; Debugging ; Dynamics (music) ; Materials science ; Chemistry ; Computer science ; Physics ; Astrobiology ; Programming language ; Physical chemistry ; Composite material ; Adsorption Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Characterization and Applications of Magnetic Nanoparticles