jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicolai Bjødstrup Palstrøm | Rune Matthiesen | Lars Melholt Rasmussen | Hans Christian Beck
Source: Biomedicines (review, 2022-01-12)
Field: Chemistry — Spectroscopy

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine. Proteome ; Biomarker discovery ; Proteomics ; Biomarker ; Medicine ; Human plasma ; Bioinformatics ; Abdominal aortic aneurysm ; Computational biology ; Biology ; Aneurysm ; Chemistry ; Surgery Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Protease and Inhibitor Mechanisms
Authors: Jing Li | Wenxin Zhang | Liying Yang | Shougen Yin
Source: Composites Communications (article, 2022-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Conductive fabrics based on carbon nanotube/Ti3C2Tx MXene/polyaniline/liquid metal quaternary composites with improved performance of EMI shielding and joule heating Materials science ; Electromagnetic shielding ; Polyaniline ; Composite material ; Electrical conductor ; EMI ; Joule heating ; Carbon nanotube ; Composite number ; Nanocomposite ; Electromagnetic interference ; Polymer ; Electrical engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Katarzyna Rybicka‐Jasińska | Tomasz Wdowik | Klaudia Łuczak | Aleksandra J. Wierzba | Olga Drapała | Dorota Gryko
Source: ACS Organic & Inorganic Au (article, 2022-07-15)
Field: Chemistry — Organic Chemistry

Porphyrins as Promising Photocatalysts for Red-Light-Induced Functionalizations of Biomolecules Red-light enables deeper material penetration, which is important for biological applications and has consequences for chemical synthesis. Therefore, the search for new photocatalysts that absorb in this region is crucial. Despite the undeniable utility of porphyrins in blue- and green-light-induced energy- and electron-transfer processes, they are also perfectly suited for red-light applications. Herein, we describe free-base porphyrins as photoredox catalysts for red-light-induced organic transformations. They can act as both photooxidants and photoreductants and can accomplish the synthesis of biaryls once merged with Pd-catalysis. The developed methodology holds promise for broader applications, as the heme-based protoporphyrin is used as a photocatalyst and reactions can be realized in aqueous conditions. Biomolecule ; Photocatalysis ; Chemistry ; Photoredox catalysis ; Protoporphyrin IX ; Red light ; Nanotechnology ; Combinatorial chemistry ; Catalysis ; Aqueous solution ; Aqueous medium ; Biochemical engineering ; Photochemistry ; Materials science ; Organic chemistry ; Photodynamic therapy Radical Photochemical Reactions ; Porphyrin and Phthalocyanine Chemistry ; CO2 Reduction Techniques and Catalysts
Authors: Bryan R. Coad | Pietro Favia | Krasimir Vasilev | Hans J. Griesser
Source: Plasma Processes and Polymers (review, 2022-09-20)
Field: Materials Science — Surfaces, Coatings and Films

Plasma polymerization for biomedical applications: A review Abstract Plasma polymers have long been of interest as thin film coatings on biomedical devices and products, to generate desirable surface properties for favorable bio‐interfacial interactions. Plasma polymers have also been used as platforms for the covalent immobilization of bioactive molecules. More recently, additional aspects have been investigated, such as selective prevention of adhesion of microbial pathogens, either via plasma polymers per se or including antimicrobial drugs. Plasma polymers have also been investigated for the release of silver ions and small organic molecules. Complementing low‐pressure plasma approaches, processes at atmospheric pressure have attracted interest recently, including for nano/biocomposite coatings. This contribution reviews the use of plasma polymers for intended biomedical applications, with a focus on more recent topic areas. Plasma polymerization ; Polymer ; Nanotechnology ; Atmospheric-pressure plasma ; Materials science ; Plasma ; Biocomposite ; Adhesion ; Polymerization ; Surface modification ; Chemical engineering ; Composite material ; Composite number ; Engineering Polymer Surface Interaction Studies ; Surface Modification and Superhydrophobicity ; Antimicrobial agents and applications
Authors: Zhuolei Zhang | Artiom Skripka | Jakob C. Dahl | Chaochao Dun | Jeffrey J. Urban | Daniel Jaque | P. James Schuck | Bruce E. Cohen | Emory M. Chan
Source: Angewandte Chemie International Edition (article, 2022-11-15)
Field: Materials Science — Materials Chemistry

Tuning Phonon Energies in Lanthanide‐doped Potassium Lead Halide Nanocrystals for Enhanced Nonlinearity and Upconversion Optical applications of lanthanide-doped nanoparticles require materials with low phonon energies to minimize nonradiative relaxation and promote nonlinear processes like upconversion. Heavy halide hosts offer low phonon energies but are challenging to synthesize as nanocrystals. Here, we demonstrate the size-controlled synthesis of low-phonon-energy KPb<sub>2</sub> X<sub>5</sub> (X=Cl, Br) nanoparticles and the ability to tune nanocrystal phonon energies as low as 128 cm<sup>-1</sup> . KPb<sub>2</sub> Cl<sub>5</sub> nanoparticles are moisture resistant and can be efficiently doped with lighter lanthanides. The low phonon energies of KPb<sub>2</sub> X<sub>5</sub> nanoparticles promote upconversion luminescence from higher lanthanide excited states and enable highly nonlinear, avalanche-like emission from KPb<sub>2</sub> Cl<sub>5</sub> : Nd<sup>3+</sup> nanoparticles. The realization of nanoparticles with tunable, ultra-low phonon energies facilitates the discovery of nanomaterials with phonon-dependent properties, precisely engineered for applications in nanoscale imaging, sensing, luminescence thermometry and energy conversion. Lanthanide ; Photon upconversion ; Phonon ; Nanomaterials ; Materials science ; Nanoparticle ; Excited state ; Luminescence ; Doping ; Nanocrystal ; Nanotechnology ; Optoelectronics ; Chemical physics ; Condensed matter physics ; Chemistry ; Atomic physics ; Physics ; Ion Luminescence Properties of Advanced Materials ; Optical properties and cooling technologies in crystalline materials ; Solid State Laser Technologies
Authors: Yabei Wu | Zhao Tang | Weiyi Xia | Weiwei Gao | Fanhao Jia | Yubo Zhang | Wenguang Zhu | Wenqing Zhang | Peihong Zhang
Source: npj Computational Materials (article, 2022-06-15)
Field: Materials Science — Materials Chemistry

Prediction of protected band edge states and dielectric tunable quasiparticle and excitonic properties of monolayer MoSi2N4 Abstract The electronic structure of two-dimensional (2D) materials are inherently prone to environmental perturbations, which may pose significant challenges to their applications in electronic or optoelectronic devices. A 2D material couples with its environment through two mechanisms: local chemical coupling and nonlocal dielectric screening effects. The local chemical coupling is often difficult to predict or control experimentally. Nonlocal dielectric screening, on the other hand, can be tuned by choosing the substrates or layer thickness in a controllable manner. Therefore, a compelling 2D electronic material should offer band edge states that are robust against local chemical coupling effects. Here it is demonstrated that the recently synthesized MoSi 2 N 4 is an ideal 2D semiconductor with robust band edge states protected from capricious environmental chemical coupling effects. Detailed many-body perturbation theory calculations are carried out to illustrate how the band edge states of MoSi 2 N 4 are shielded from the direct chemical coupling effects, but its quasiparticle and excitonic properties can be modulated through the nonlocal dielectric screening effects. This unique property, together with the moderate band gap and the thermodynamic and mechanical stability of this material, paves the way for a range of applications of MoSi 2 N 4 in areas including energy, 2D electronics, and optoelectronics. Quasiparticle ; Coupling (piping) ; Dielectric ; Semiconductor ; Materials science ; Band gap ; Optoelectronics ; Electronic structure ; Enhanced Data Rates for GSM Evolution ; Exciton ; Monolayer ; Electronics ; Condensed matter physics ; Chemical physics ; Nanotechnology ; Physics ; Chemistry ; Telecommunications ; Computer science ; Physical chemistry ; Composite material 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Avinash R. Pai | P. A. Nizam | Binumol Thankan | Nandakumar Gopakumar | Maciej Jaroszewski | Claudio Paoloni | Nandakumar Kalarikkal | Sabu Thomas
Source: Energies (article, 2022-05-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Progress in Electromagnetic Interference Shielding Performance of Porous Polymer Nanocomposites—A Review The urge to develop high-speed data transfer technologies for futuristic electronic and communication devices has led to more incidents of serious electromagnetic interference and pollution. Over the past decade, there has been burgeoning research interests to design and fabricate high-performance porous EM shields to tackle this undesired phenomenon. Polymer nanocomposite foams and aerogels offer robust, flexible and lightweight architectures with tunable microwave absorption properties and are foreseen as potential candidates to mitigate electromagnetic pollution. This review covers various strategies adopted to fabricate 3D porous nanocomposites using conductive nanoinclusions with suitable polymer matrices, such as elastomers, thermoplastics, bioplastics, conducting polymers, polyurethanes, polyimides and nanocellulose. Special emphasis has been placed on novel 2D materials such as MXenes, that are envisaged to be the future of microwave-absorbing materials for next-generation electronic devices. Strategies to achieve an ultra-low percolation threshold using environmentally benign and facile processing techniques have been discussed in detail. Materials science ; Nanocomposite ; Nanocellulose ; Electromagnetic shielding ; Nanotechnology ; Electromagnetic interference ; Conductive polymer ; Electronics ; Polymer nanocomposite ; Polymer ; Composite material ; Computer science ; Electrical engineering ; Telecommunications ; Engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Sara Mantach | Abdulla Lutfi | Hamed Moradi Tavasani | Ahmed Ashraf | Ayman El‐Hag | Behzad Kordi
Source: Energies (review, 2022-07-08)
Field: Materials Science — Materials Chemistry

Deep Learning in High Voltage Engineering: A Literature Review Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators. Deep learning ; Computer science ; Artificial intelligence ; High voltage ; Transformer ; Process (computing) ; Machine learning ; Feature extraction ; Voltage ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Andrew Dickson White
Source: Living Journal of Computational Molecular Science (article, 2022-06-16)
Field: Materials Science — Materials Chemistry

Deep Learning for Molecules and Materials Deep learning is becoming a standard tool in chemistry and materials science. Although there are learning materials available for deep learning, none cover the applications in chemistry and materials science or the peculiarities of working with molecules. The textbook described here provides a systematic and applied introduction to the latest research in deep learning in chemistry and materials science. It covers the math fundamentals, the requisite machine learning, the common neural network architectures used today, and the details necessary to be a practitioner of deep learning. The textbook is a living document and will be updated as the rapidly changing deep learning field evolves. Deep learning ; Artificial intelligence ; Computer science ; Learning sciences ; Data science ; Nanotechnology ; Cognitive science ; Mathematics education ; Experiential learning ; Materials science ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Qingzhao Chu | Kai Luo | Dongping Chen
Source: The Journal of Physical Chemistry Letters (article, 2022-05-06)
Field: Materials Science — Materials Chemistry

Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation <i>Ab initio</i> molecular dynamics (AIMD) is an established method for revealing the reactive dynamics of complex systems. However, the high computational cost of AIMD restricts the explorable length and time scales. Here, we develop a fundamentally different approach using molecular dynamics simulations powered by a neural network potential to investigate complex reaction networks. This potential is trained via a workflow combining AIMD and interactive molecular dynamics in virtual reality to accelerate the sampling of rare reactive processes. A panoramic visualization of the complex reaction networks for decomposition of a novel high explosive (ICM-102) is achieved without any predefined reaction coordinates. The study leads to the discovery of new pathways that would be difficult to uncover if established methods were employed. These results highlight the power of neural network-based molecular dynamics simulations in exploring complex reaction mechanisms under extreme conditions at the <i>ab initio</i> level, pushing the limit of theoretical and computational chemistry toward the realism and fidelity of experiments. Computer science ; Molecular dynamics ; Complex network ; Workflow ; Artificial neural network ; Network dynamics ; Virtual screening ; Visualization ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Umberto Raucci | Valerio Rizzi | Michele Parrinello
Source: The Journal of Physical Chemistry Letters (article, 2022-02-04)
Field: Materials Science — Materials Chemistry

Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the-fly probability enhanced sampling method to drive reaction discovery runs. Once different chemical products are determined, we construct an ad-hoc neural network-based collective variable to improve sampling, and finally we refine the results using the free energy perturbation theory and a more accurate Hamiltonian. We apply this strategy to both intramolecular and intermolecular reactions. Our workflow requires minimal user input and extends the power of <i>ab initio</i> molecular dynamics to explore and characterize the reaction space. Exploit ; Computer science ; Chemical space ; Intramolecular force ; Modular design ; Workflow ; Chemistry ; Sampling (signal processing) ; Graph ; Intermolecular force ; Theoretical computer science ; Computational chemistry ; Drug discovery ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xinyu Lu | Peng Liu | Krishna Bisetty | Yue Cai | Xuemin Duan | Yangping Wen | Yifu Zhu | Liangmei Rao | Quan Xu | Jingkun Xu
Source: Journal of Electroanalytical Chemistry (article, 2022-07-19)
Field: Chemistry — Inorganic Chemistry

An emerging machine learning strategy for electrochemical sensor and supercapacitor using carbonized metal–organic framework Supercapacitor ; Electrochemistry ; Detection limit ; Carbonization ; Electrochemical gas sensor ; Artificial neural network ; Chemistry ; Capacitance ; Electrode ; Metal-organic framework ; Adsorption ; Nanotechnology ; Computer science ; Materials science ; Artificial intelligence ; Chromatography Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Carl‐Friedrich Schön | Steffen van Bergerem | Christian Mattes | Aakash Yadav | Martin Grohe | Leif Kobbelt | Matthias Wuttig
Source: Science Advances (article, 2022-11-23)
Field: Materials Science — Materials Chemistry

Classification of properties and their relation to chemical bonding: Essential steps toward the inverse design of functional materials To design advanced functional materials, different concepts are currently pursued, including machine learning and high-throughput calculations. Here, a different approach is presented, which uses the innate structure of the multidimensional property space. Clustering algorithms confirm the intricate structure of property space and relate the different property classes to different chemical bonding mechanisms. For the inorganic compounds studied here, four different property classes are identified and related to ionic, metallic, covalent, and recently identified metavalent bonding. These different bonding mechanisms can be quantified by two quantum chemical bonding descriptors, the number of electrons transferred and the number of electrons shared between adjacent atoms. Hence, we can link these bonding descriptors to the corresponding property portfolio, turning bonding descriptors into property predictors. The close relationship between material properties and quantum chemical bonding descriptors can be used for an inverse material design, identifying particularly promising materials based on a set of target functionalities. Chemical bond ; Property (philosophy) ; Chemical space ; Ionic bonding ; Computer science ; Quantum chemical ; Cluster analysis ; Covalent bond ; Inverse ; Materials science ; Molecule ; Chemical physics ; Biological system ; Chemistry ; Mathematics ; Machine learning ; Ion ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Robert R. Schade | Tobias Kenter | Hossam Elgabarty | Michael Lass | Ole Schütt | Alfio Lazzaro | Hans Pabst | Stephan Mohr | Jürg Hutter | Thomas D. Kühne | Christian Plessl
Source: Parallel Computing (article, 2022-03-05)
Field: Materials Science — Materials Chemistry

Towards electronic structure-based ab-initio molecular dynamics simulations with hundreds of millions of atoms We push the boundaries of electronic structure-based ab-initio molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical force-field methods or novel neural network and machine learning potentials. We achieve this breakthrough by combining innovations in linear-scaling AIMD, efficient and approximate sparse linear algebra, low and mixed-precision floating-point computation on GPUs, and a compensation scheme for the errors introduced by numerical approximations. The core of our work is the non-orthogonalized local submatrix method (NOLSM), which scales very favorably to massively parallel computing systems and translates large sparse matrix operations into highly parallel, dense matrix operations that are ideally suited to hardware accelerators. We demonstrate that the NOLSM method, which is at the center point of each AIMD step, is able to achieve a sustained performance of 324 PFLOP/s in mixed FP16/FP32 precision corresponding to an efficiency of 67.7% when running on 1536 NVIDIA A100 GPUs. Linear scale ; Massively parallel ; Computer science ; Computational science ; Scaling ; Computation ; Linear algebra ; Electronic structure ; Matrix (chemical analysis) ; Field (mathematics) ; Molecular dynamics ; Ab initio ; Parallel computing ; Algorithm ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Advanced NMR Techniques and Applications