jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Benjamin A. Neely | Viktoria Dorfer | Lennart Martens | Isabell Bludau | Robbin Bouwmeester | Sven Degroeve | Eric W. Deutsch | Siegfried Gessulat | Lukas Käll | Pawel Palczynski | Samuel Payne | Tobias Greisager Rehfeldt | Tobias Schmidt | Veit Schwämmle | Julian Uszkoreit | Juan Antonio Vizcaíno | Mathias Wilhelm | Magnus Palmblad
Source: Journal of Proteome Research (review, 2023-02-06)
Field: Chemistry — Spectroscopy

Toward an Integrated Machine Learning Model of a Proteomics Experiment In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research. Proteomics ; Computer science ; Artificial intelligence ; Machine learning ; Computational biology ; Chemistry ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Machine Learning and Data Classification
Authors: Tanveer Ahmed | Jiajia Zha | Kris Ke-Hsien Lin | Hao‐Chung Kuo | Chaoliang Tan | Der‐Hsien Lien
Source: Advanced Materials (review, 2023-02-20)
Field: Materials Science — Materials Chemistry

Bright and Efficient Light‐Emitting Devices Based on 2D Transition Metal Dichalcogenides 2D monolayer transition metal dichalcogenides (TMDCs) show great promise for the development of next-generation light-emitting devices owing to their unique electronic and optoelectronic properties. The dangling-bond-free surface and direct-bandgap structure of monolayer TMDCs allow for near-unity photoluminescence quantum efficiencies. The excellent mechanical and optical characteristics of 2D TMDCs offer great potential to fabricate TMDC-based light-emitting diodes (LEDs) featuring good flexibility and transparency. Great progress has been made in the fabrication of bright and efficient LEDs with varying device structures. In this review, the aim is to provide a comprehensive summary of the state-of-the-art progress made in the construction of bright and efficient LEDs based on 2D TMDCs. After a brief introduction to the research background, the preparation of 2D TMDCs used for LEDs is briefly discussed. The requirements and the corresponding challenges to achieve bright and efficient LEDs based on 2D TMDCs are introduced. Thereafter, various strategies to enhance the brightness of monolayer 2D TMDCs are described. Following that, the carrier-injection schemes enabling bright and efficient TMDC-based LEDs along with the device performance are summarized. Finally, the challenges and future prospects regarding the accomplishment of TMDC-LEDs with ultimate brightness and efficiency are discussed. Light-emitting diode ; Materials science ; Optoelectronics ; Monolayer ; Diode ; Photoluminescence ; Nanotechnology ; Brightness ; Optics 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Jonas Bals | Matthias Epple
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning for automated size and shape analysis of nanoparticles in scanning electron microscopy The automated analysis of nanoparticles, imaged by scanning electron microscopy, was implemented by a deep-learning (artificial intelligence) procedure based on convolutional neural networks (CNNs). It is possible to extract quantitative information on particle size distributions and particle shapes from pseudo-three-dimensional secondary electron micrographs (SE) as well as from two-dimensional scanning transmission electron micrographs (STEM). After separation of particles from the background (segmentation), the particles were cut out from the image to be classified by their shape (<i>e.g.</i> sphere or cube). The segmentation ability of STEM images was considerably enhanced by introducing distance- and intensity-based pixel weight loss maps. This forced the neural network to put emphasis on areas which separate adjacent particles. Partially covered particles were recognized by training and excluded from the analysis. The separation of overlapping particles, quality control procedures to exclude agglomerates, and the computation of quantitative particle size distribution data (equivalent particle diameter, Feret diameter, circularity) were included into the routine. Scanning electron microscope ; Nanoparticle ; Electron microscope ; Materials science ; Nanotechnology ; Microscopy ; Electron ; Chemical engineering ; Optics ; Physics ; Engineering ; Composite material ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Claudio Zeni | Robert Pinsler | Daniel Zügner | Andrew Fowler | Matthew K. Horton | Xiang Fu | Sasha Shysheya | Jonathan Crabbé | Lixin Sun | Jake A. Smith | Nguyen, Bichlien | Schulz, Hannes | Lewis, Sarah | Huang, Chin-Wei | Lu, Ziheng | Zhou, Yichi | Yang, Han | Hao, Hongxia | Li, Jielan | Tomioka, Ryota | Xie, Tian
Source: arXiv (Cornell University) (preprint, 2023-12-06)
Field: Materials Science — Materials Chemistry

MatterGen: a generative model for inorganic materials design The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly generating entirely novel materials given desired property constraints. Despite recent progress, current generative models have low success rate in proposing stable crystals, or can only satisfy a very limited set of property constraints. Here, we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. To enable this, we introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice. We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset. Compared to prior generative models, structures produced by MatterGen are more than twice as likely to be novel and stable, and more than 15 times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, novel materials with desired chemistry, symmetry, as well as mechanical, electronic and magnetic properties. Finally, we demonstrate multi-property materials design capabilities by proposing structures that have both high magnetic density and a chemical composition with low supply-chain risk. We believe that the quality of generated materials and the breadth of MatterGen's capabilities represent a major advancement towards creating a universal generative model for materials design. Generative grammar ; Generative Design ; Generative model ; Computer science ; Property (philosophy) ; Fine-tuning ; Materials science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence
Authors: Farnaz Zadehahmadi | Nathan T. Eden | Hamidreza Mahdavi | Kristina Konstas | James I. Mardel | Mahdokht Shaibani | Parama Chakraborty Banerjee | Matthew R. Hill
Source: Environmental Science Water Research & Technology (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Removal of metals from water using MOF-based composite adsorbents This review article focuses on metal–organic framework (MOF) composites and the impact of mixing MOFs with other materials, such as polymers, carbon-based materials, and magnetic particles, to increase their performance to remove metals from water. Metal-organic framework ; Adsorption ; Materials science ; Composite number ; Water treatment ; Mixing (physics) ; Metal ; Polymer ; Carbon fibers ; Chemical engineering ; Composite material ; Waste management ; Metallurgy ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Mercury impact and mitigation studies
Authors: Martina Ferri | Kseniya Papchenko | Micaela Degli Esposti | Gianluca Tondi | María Grazia De Angelis | Davide Morselli | Paola Fabbri
Source: ACS Applied Materials & Interfaces (article, 2023-06-02)
Field: Materials Science — Biomaterials

Fully Biobased Polyhydroxyalkanoate/Tannin Films as Multifunctional Materials for Smart Food Packaging Applications Fully biobased and biodegradable materials have attracted a growing interest in the food packaging sector as they can help to reduce the negative impact of fossil-based plastics on the environment. Moreover, the addition of functionalities to these materials by introducing active molecules has become an essential requirement to create modern packaging able to extend food's shelf-life while informing the consumer about food quality and freshness. In this study, we present an innovative bioplastic formulation for food packaging based on poly(hydroxybutyrate-<i>co</i>-valerate) (PHBV) and tannins as multifunctional additives. As a proof of concept, PHBV/tannin films were prepared by solvent casting, increasing the tannin content from 1 to 10 per hundred of resin (phr). Formic acid was used to reach a homogeneous distribution of the hydrophilic tannins into hydrophobic PHBV, which is remarkably challenging by using other solvents. Thanks to their well-known properties, the effect of tannins on the antioxidant, UV protection, and gas barrier properties of PHBV was evaluated. Samples containing 5 phr bioadditive revealed the best combination of these properties, also maintaining good transparency. Differential scanning calorimetry (DSC) investigations revealed that films are suitable for application from the fridge to potentially high temperatures for food heating (up to 200 °C). Tensile tests have also shown that Young's modulus (900-1030 MPa) and tensile strength (20 MPa) are comparable with those of the common polymers and biopolymers for packaging. Besides the improvement of the PHBV properties for extending food's shelf-life, it was also observed that PHBV/tannin could colorimetrically detect ammonia vapors, thus making this material potentially applicable as a smart indicator for food spoilage (e.g., detection of fish degradation). The presented outcomes suggest that tannins can add multifunctional properties to a polymeric material, opening up a new strategy to obtain an attractive alternative to petroleum-based plastics for smart food packaging applications. Materials science ; Food packaging ; Bioplastic ; Differential scanning calorimetry ; Tannin ; Ultimate tensile strength ; Active packaging ; Shelf life ; Polymer ; Composite material ; Food science ; Waste management ; Chemistry biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Carbon dioxide utilization in catalysis
Authors: Pietro Aceti | Luca Carminati | Paolo Bettini | Giuseppe Sala
Source: Composite Structures (article, 2023-05-15)
Field: Materials Science — Polymers and Plastics

Hygrothermal ageing of composite structures. Part 1: Technical review The aim of this work is to review the literature of hygrothermal ageing in composite structures, focusing on the effects of moisture absorption on mechanical properties, damage behavior and degradation mechanisms responsible for changes in performance. Potential causes that can lead to failures are detected and the main variables that influence the ageing are taken into account: these include the surrounding environment and climate conditions, which appear to be determinant. Secondly, a case study analysis on previous accidents involving aircraft composite structures damaged by moisture is presented. This reveals that some components, such as sandwich panels and adhesive-bonded-joints, are particularly affected by hygrothermal ageing, and thus require a detailed assessment in terms of humidity deterioration. Further insights on the main industrial applications of these components, such as wind and tidal turbine, are showcased and crucial issues are highlighted. Moreover bio-composites behaviour in harsh environments are analyzed, showing drawbacks and advantages. Composite number ; Moisture ; Ageing ; Work (physics) ; Materials science ; Humidity ; Structural engineering ; Environmental science ; Composite material ; Forensic engineering ; Engineering ; Mechanical engineering ; Meteorology Natural Fiber Reinforced Composites ; Cellular and Composite Structures ; Mechanical Behavior of Composites
Authors: Tongyu Huo | Xinyi Zhao | Zengrui Cheng | Jialiang Wei | Minghui Zhu | Xiaodong Dou | Ning Jiao
Source: Acta Pharmaceutica Sinica B (article, 2023-11-18)
Field: Chemistry — Organic Chemistry

Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing Synthetic chemistry plays an indispensable role in drug discovery, contributing to hit compounds identification, lead compounds optimization, candidate drugs preparation, and so on. As Nobel Prize laureate James Black emphasized, "the most fruitful basis for the discovery of a new drug is to start with an old drug"<sup>1</sup>. Late-stage modification or functionalization of drugs, natural products and bioactive compounds have garnered significant interest due to its ability to introduce diverse elements into bioactive compounds promptly. Such modifications alter the chemical space and physiochemical properties of these compounds, ultimately influencing their potency and druggability. To enrich a toolbox of chemical modification methods for drug discovery, this review focuses on the incorporation of halogen, oxygen, and nitrogen-the ubiquitous elements in pharmacophore components of the marketed drugs-through late-stage modification in recent two decades, and discusses the state and challenges faced in these fields. We also emphasize that increasing cooperation between chemists and pharmacists may be conducive to the rapid discovery of new activities of the functionalized molecules. Ultimately, we hope this review would serve as a valuable resource, facilitating the application of late-stage modification in the construction of novel molecules and inspiring innovative concepts for designing and building new drugs. Druggability ; Drug discovery ; Toolbox ; Pharmacophore ; Chemistry ; Combinatorial chemistry ; Chemical space ; Computer science ; Nanotechnology ; Computational biology ; Biology ; Biochemistry ; Materials science Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods ; Click Chemistry and Applications
Authors: Liwen Li | Yining Shen | Zhongmin Tang | Yuwen Yang | Zi Fu | Dalong Ni | Xiaojun Cai
Source: Exploration (review, 2023-07-20)
Field: Materials Science — Materials Chemistry

Engineered nanodrug targeting oxidative stress for treatment of acute kidney injury Acute kidney injury (AKI) is a clinical syndrome characterized by a rapid decline in renal function, and is associated with a high risk of death. Many pathological changes happen in the process of AKI, including crucial alterations to oxidative stress levels. Numerous efforts have thus been made to develop effective medicines to scavenge excess reactive oxygen species (ROS). However, researchers have encountered several significant challenges, including unspecific biodistribution, high biotoxicity, and in vivo instability. To address these problems, engineered nanoparticles have been developed to target oxidative stress and treat AKI. This review thoroughly discusses the methods that empower nanodrugs to specifically target the glomerular filtration barrier and presents the latest achievements in engineering novel ROS-scavenging nanodrugs in clustered sections. The analysis of each study's breakthroughs and imperfections visualizes the progress made in developing effective nanodrugs with specific biodistribution and oxidative stress-targeting capabilities. This review fills the blank of a comprehensive outline over current progress in applying nanotechnology to treat AKI, providing potential insights for further research. Oxidative stress ; Acute kidney injury ; Medicine ; Intensive care medicine ; Cancer research ; Internal medicine Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Sofia Jeniffer Rajasekaran | Andrews Nirmala Grace | George Jacob | Abdullah N. Alodhayb | Saravanan Pandiaraj | Vimala Raghavan
Source: Catalysts (article, 2023-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Investigation of Different Aqueous Electrolytes for Biomass-Derived Activated Carbon-Based Supercapacitors The present work reports the synthesis of biomass derived activated carbon and its electrochemical behaviour in different electrolytes. Ricinus communis shell (RCS) was used as a raw material in this study for the synthesis of activated carbon (AC) following a high-temperature activation procedure using potassium hydroxide as the activating agent. The physical and structural characterization of the prepared Ricinus communis shell-derived activated carbon (RCS-AC) was carried by Brunauer-Emmett-Teller analysis, X-ray diffraction analysis, Fourier Transform Infrared Spectroscopy, Raman Spectroscopy and Scanning Electron Microscopy. The synthesized AC was electrochemically characterized using various techniques such as Cyclic voltammetry (CV), galvanostatic charge–discharge (GCD) tests, and Electrochemical impedance spectroscopy (EIS) measurements in different aqueous electrolytes (KOH, H2SO4, and Na2SO4). The results show that the double layer properties of the RCS-AC material in different electrolytes are distinct. In specific, the working electrode tested in 3 M KOH showed excellent electrochemical performance. It demonstrated a specific capacitance of 137 F g−1 (at 1 A g−1 in 3 M KOH) and exhibited high energy and power densities of 18.2 W hkg−1 and 663.4 W kg−1, respectively. The observed capacitance in 3 M KOH remains stable with 97.2% even after 5000 continuous charge and discharge cycles, indicating long-term stability. The study confirmed that the synthesized RCS-derived activated carbon (RCS-AC) exhibits good stability and physicochemical characteristics, making them commercially promising and appropriate for energy storage applications. Supercapacitor ; Potassium hydroxide ; Materials science ; Activated carbon ; Dielectric spectroscopy ; Cyclic voltammetry ; Electrolyte ; Electrochemistry ; Fourier transform infrared spectroscopy ; Chemical engineering ; Raman spectroscopy ; Aqueous solution ; Capacitance ; Analytical Chemistry (journal) ; Electrode ; Chemistry ; Chromatography ; Organic chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Haoran Li | Diego Serrano | Thomas Guillod | Shukai Wang | Evan Dogariu | Andrew B. Nadler | Min Luo | Vineet Bansal | Niraj K. Jha | Yuxin Chen | Charles R. Sullivan | Minjie Chen
Source: IEEE Transactions on Power Electronics (article, 2023-08-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics This paper applies machine learning to power magnetics modeling. We first introduce an open-source database – MagNet – which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions, consisting of more than 500,000 data points in its current state. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling the core losses and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$B$</tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H$</tex-math></inline-formula> loops. The neural network allows multiple factors that may influence the magnetic characteristics to be modeled in a unified framework, where the non-linear behaviors are captured with high accuracy and high generality. Neural network models are found to be effective in compressing the measurement data and predicting the material characteristics, paving the way for “neural networks as datasheets” to assist power magnetics design. Transfer learning is applied to the training of neural network models to further reduce the data size requirement while maintaining sufficient model accuracy. Artificial neural network ; Artificial intelligence ; Computer science ; Machine learning ; Power (physics) ; Magnet ; Notation ; Algorithm ; Computer engineering ; Electrical engineering ; Engineering ; Mathematics ; Physics Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Magnetic properties of thin films