jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Constant A. Schouder | Adam S. Chatterley | James D. Pickering | Henrik Stapelfeldt
Source: Annual Review of Physical Chemistry (review, 2022-01-26)
Field: Chemistry — Spectroscopy

Laser-Induced Coulomb Explosion Imaging of Aligned Molecules and Molecular Dimers We discuss how Coulomb explosion imaging (CEI), triggered by intense femtosecond laser pulses and combined with laser-induced alignment and covariance analysis of the angular distributions of the recoiling fragment ions, provides new opportunities for imaging the structures of molecules and molecular complexes. First, focusing on gas phase molecules, we show how the periodic torsional motion of halogenated biphenyl molecules can be measured in real time by timed CEI, and how CEI of one-dimensionally aligned difluoroiodobenzene molecules can uniquely identify four structural isomers. Next, focusing on molecular complexes formed inside He nano-droplets, we show that the conformations of noncovalently bound dimers or trimers, aligned in one or three dimensions, can be determined by CEI. Results presented for homodimers of CS<sub>2</sub>, OCS, and bromobenzene pave the way for femtosecond time-resolved structure imaging of molecules undergoing bimolecular interactions and ultimately chemical reactions. Coulomb explosion ; Femtosecond ; Molecule ; Laser ; Chemical physics ; Molecular dynamics ; Molecular physics ; Ion ; Bromobenzene ; Chemistry ; Atomic physics ; Materials science ; Physics ; Optics ; Computational chemistry ; Ionization Mass Spectrometry Techniques and Applications ; Laser-Matter Interactions and Applications ; Spectroscopy and Laser Applications
Authors: Kai Yu | Ye Wang | Yongqiang Zheng | Zekun Liu | Qingfeng Zhang | Siyu Wang | Qi Zhao | Xiao-Long Zhang | Xiaoxing Li | Rui‐Hua Xu | Zexian Liu
Source: Nucleic Acids Research (article, 2022-09-27)
Field: Chemistry — Spectroscopy

qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast Post-translational modifications (PTMs) are critical molecular mechanisms that regulate protein functions temporally and spatially in various organisms. Since most PTMs are dynamically regulated, quantifying PTM events under different states is crucial for understanding biological processes and diseases. With the rapid development of high-throughput proteomics technologies, massive quantitative PTM proteome datasets have been generated. Thus, a comprehensive one-stop data resource for surfing big data will benefit the community. Here, we updated our previous phosphorylation dynamics database qPhos to the qPTM (http://qptm.omicsbio.info). In qPTM, 11 482 553 quantification events among six types of PTMs, including phosphorylation, acetylation, glycosylation, methylation, SUMOylation and ubiquitylation in four different organisms were collected and integrated, and the matched proteome datasets were included if available. The raw mass spectrometry based false discovery rate control and the recurrences of identifications among datasets were integrated into a scoring system to assess the reliability of the PTM sites. Browse and search functions were improved to facilitate users in swiftly and accurately acquiring specific information. The results page was revised with more abundant annotations, and time-course dynamics data were visualized in trend lines. We expected the qPTM database to be a much more powerful and comprehensive data repository for the PTM research community. Biology ; SUMO protein ; Proteome ; Computational biology ; Proteomics ; Proteogenomics ; Database ; Phosphorylation ; Resource (disambiguation) ; Glycosylation ; Computer science ; Bioinformatics ; Genomics ; Ubiquitin ; Genome ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Machine Learning in Bioinformatics
Authors: Subhrajit Mukherjee | Elad Koren
Source: Israel Journal of Chemistry (article, 2022-01-05)
Field: Materials Science — Materials Chemistry

Indium Selenide (In<sub>2</sub>Se<sub>3</sub>) – An Emerging Van‐der‐Waals Material for Photodetection and Non‐Volatile Memory Applications Abstract The search for ultrathin and robust ferroelectrics leads to few promising two‐dimensional (2D) materials. In 2 Se 3 has drawn special attention owing to the existence of intercoupled in‐plane (IP) and out‐of‐plane (OOP) ferroelectricity in monolayer form, which makes it a potential candidate for emerging artificial intelligence, information processing and memory applications. In addition, the high optical absorption and phase‐dependent visible to infrared bandgap become advantageous from optoelectronics point‐of‐view. This unique ferroelectric and optoelectronic coupling further leads to explore atomic‐scale multifunctional devices. In this review, we summarized the progress of non‐volatile memory (NVM) and photodetector devices, and their intercoupled applications using 2D In 2 Se 3 . We expect that this review will provide an insight towards the promising future of 2D ferroelectric‐(opto)electronics and motivate researchers for further development towards industrial scale. Indium ; Ferroelectricity ; Photodetector ; Chemistry ; Nanotechnology ; Optoelectronics ; van der Waals force ; Monolayer ; Photodetection ; Non-volatile memory ; Band gap ; Materials science 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Huimeng Wang | Yi Fan | Yaqi Hou | Baiyi Chen | Jinmei Lei | Shijie Yu | Xinyu Chen | Xu Hou
Source: Nature Communications (article, 2022-04-07)
Field: Materials Science — Materials Chemistry

Host-guest liquid gating mechanism with specific recognition interface behavior for universal quantitative chemical detection Universal visual quantitative chemical detection technology has emerged as an increasingly crucial tool for convenient testing with immediate results in the fields of environmental assessment, homeland security, clinical drug testing and health care, particularly in resource-limited settings. Here, we show a host-guest liquid gating mechanism to translate molecular interface recognition behavior into visually quantifiable detection signals. Quantitative chemical detection is achieved, which has obvious advantages for constructing a portable, affordable, on-site sensing platform to enable the visual quantitative testing of target molecules without optical/electrical equipment. Experiments and theoretical calculations confirm the specificity and scalability of the system. This mechanism can also be tailored by the rational design of host-guest complexes to quantitatively and visually detect various molecules. With the advantages of versatility and freedom from additional equipment, this detection mechanism has the potential to revolutionize environmental monitoring, food safety analysis, clinical drug testing, and more. Computer science ; Mechanism (biology) ; Interface (matter) ; Scalability ; Host (biology) ; Gating ; Human–computer interaction ; Database ; Neuroscience Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques
Authors: Jaruwan Amtawong | Andy I. Nguyen | T. Don Tilley
Source: Journal of the American Chemical Society (article, 2022-01-21)
Field: Materials Science — Materials Chemistry

Mechanistic Aspects of Cobalt–Oxo Cubane Clusters in Oxidation Chemistry Cobalt oxides are recognized as one of the most efficient earth-abundant catalysts for challenging oxidation chemistry, with substrates ranging from water to organic compounds. In these oxidations, cobalt-oxo species with formal oxidation states greater than 3 are commonly invoked as reactive intermediates. However, there is a dearth of mechanistic information regarding how these high-valent cobalt catalysts operate. This Perspective describes how the study of molecular cobalt oxo clusters, with an emphasis on [Co<sub>4</sub>O<sub>4</sub>] oxo cubane complexes, has helped to shed light on the operative mechanisms of cobalt-catalyzed oxidation reactions. Implications for high-valent Co<sup>IV</sup>-oxo and Co<sup>V</sup>-oxo intermediates and remaining mechanistic questions concerning how these intermediates mediate O-O bond formation are also discussed. Furthermore, structural modifications of these oxo cubane clusters (i.e., incorporation of heteroatoms and modulation of ligands) have provided insight into multimetallic cooperativity, but the influence of such metal-metal interactions on oxidation activity remains to be explored. A more detailed understanding of these structure-activity relationships may enable fine-tuning of reactivity and stability of synthetic multimetallic catalysts for energy storage and challenging organic transformations. Chemistry ; Cobalt ; Cubane ; Catalysis ; Cooperativity ; Heteroatom ; Reactivity (psychology) ; Combinatorial chemistry ; Molecule ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Metal-Catalyzed Oxygenation Mechanisms ; Electrocatalysts for Energy Conversion
Authors: Anwang Dong | Zhenjie Mu | Xiangjian Meng | Shuai Li | Jiani Li | Lu Dai | Jianning Lv | Pengfei Li | Bo Wang
Source: Chemical Engineering Journal (article, 2022-04-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fine-tuning the electromagnetic parameters of 2D conjugated metal-organic framework semiconductors for anti-electromagnetic interference in the Ku band Reflection loss ; Materials science ; Absorption (acoustics) ; Electromagnetic radiation ; Electromagnetic interference ; Optoelectronics ; EMI ; Absorption band ; Ku band ; X band ; Optics ; Composite material ; Physics ; Electronic engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Mariana Acosta | Marvin D. Santiago | Jennifer A. Irvin
Source: Materials (review, 2022-12-09)
Field: Materials Science — Polymers and Plastics

Electrospun Conducting Polymers: Approaches and Applications Inherently conductive polymers (CPs) can generally be switched between two or more stable oxidation states, giving rise to changes in properties including conductivity, color, and volume. The ability to prepare CP nanofibers could lead to applications including water purification, sensors, separations, nerve regeneration, wound healing, wearable electronic devices, and flexible energy storage. Electrospinning is a relatively inexpensive, simple process that is used to produce polymer nanofibers from solution. The nanofibers have many desirable qualities including high surface area per unit mass, high porosity, and low weight. Unfortunately, the low molecular weight and rigid rod nature of most CPs cannot yield enough chain entanglement for electrospinning, instead yielding polymer nanoparticles via an electrospraying process. Common workarounds include co-extruding with an insulating carrier polymer, coaxial electrospinning, and coating insulating electrospun polymer nanofibers with CPs. This review explores the benefits and drawbacks of these methods, as well as the use of these materials in sensing, biomedical, electronic, separation, purification, and energy conversion and storage applications. Materials science ; Electrospinning ; Nanofiber ; Polymer ; Nanotechnology ; Coating ; Porosity ; Composite material Conducting polymers and applications ; Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Susanne Kirchner | Anna‐Lena Leistner | Peter Gödtel | Angelika Seliwjorstow | Sven Weber | Johannes Karcher | Martin Nieger | Zbigniew Pianowski
Source: Nature Communications (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Hemipiperazines as peptide-derived molecular photoswitches with low-nanomolar cytotoxicity Molecular photoswitches transform light energy into reversible structural changes. Their combination with known pharmacophores often allows for photomodulation of the biological activity. The effort to apply such compounds in photopharmacology as light-activated pro-drugs is, however, hampered by serious activity reduction upon pharmacophore modifications, or limited biostability. Here we report that a potent antimitotic agent plinabulin and its derivatives demonstrate up to 56-fold reversible activity photomodulation. Alternatively, irreversible photoactivation with cyan light can enhance the cytotoxicity up to three orders of magnitude-all without compromising the original activity level, as the original pharmacophore structure is unchanged. This occurs due to the presence of a peptide-derived photoswitchable motif hemipiperazine inside the plinabulin scaffold. Furthermore, we systematically describe photochromism of these thermally stable and biocompatible hemipiperazines, as well as a photoswitchable fluorophore derived from plinabulin. The latter may further expand the applicability of hemipiperazine photochromism towards super-resolution microscopy. Pharmacophore ; Photochromism ; Cytotoxicity ; Chemistry ; Peptide ; Biophysics ; Combinatorial chemistry ; Biocompatible material ; Fluorophore ; Photoswitch ; Nanotechnology ; Stereochemistry ; In vitro ; Photochemistry ; Materials science ; Biochemistry Photochromic and Fluorescence Chemistry ; Nanoplatforms for cancer theranostics ; Click Chemistry and Applications
Authors: Neeru Bala | Bheem Khan | Kamaljit Singh | Palwinder Singh | Abhinav Pratap Singh | Anup Thakur
Source: Materials Advances (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Recent advances in doped Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> thin film based phase change memories Nowadays, chalcogenide-based phase change materials (PCMs) are proving themselves superior in the category of memory devices due to a combination of their unique set of properties. Doping ; Materials science ; Thin film ; Antimony ; Germanium ; Phase (matter) ; Phase change ; Phase-change memory ; Optoelectronics ; Engineering physics ; Nanotechnology ; Silicon ; Metallurgy ; Physics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Nonlinear Optical Materials Studies
Authors: Pengfei Cai | Jun Li | Xinbao Wu | Zhaoyang Li | Jie Shen | Jing‐Jun Nie | Zhenduo Cui | Dafu Chen | Yanqin Liang | Shengli Zhu | Shuilin Wu
Source: Rare Metals (article, 2022-09-20)
Field: Materials Science — Materials Chemistry

ALD‐induced TiO <sub>2</sub> /Ag nanofilm for rapid surface photodynamic ion sterilization Abstract The daily life of people in the intelligent age is inseparable from electronic device, and a number of bacteria on touch screens are increasingly threatening the health of users. Herein, a photocatalytic TiO 2 /Ag thin film was synthesized on a glass by atomic layer deposition and subsequent in situ reduction. Ultraviolet–visible (UV‐Vis) spectra showed that this film can harvest the simulated solar light more efficiently than that of pristine TiO 2 . The antibacterial tests in vitro showed that the antibacterial efficiency of the TiO 2 /Ag film against S. aureus and E. coli was 98.2% and 98.6%, under visible light irradiation for 5 min. The underlying mechanism was that the in‐situ reduction of Ag on the surface of TiO 2 reduced the bandgap of TiO 2 from 3.44 to 2.61 eV due to the formation of Schottky heterojunction at the interface between TiO 2 and Ag. Thus, TiO 2 /Ag can generate more reactive oxygen species for bacterial inactivation on the surface of electronic screens. More importantly, the TiO 2 /Ag film had great biocompatibility with/without light irradiation. The platform not only provides a more convenient choice for the traditional antibacterial mode but also has limitless possibilities for application in the field of billions of touch screens. Materials science ; Photocatalysis ; Irradiation ; Heterojunction ; Visible spectrum ; Biocompatibility ; Ultraviolet ; Nanotechnology ; Photochemistry ; Band gap ; Optoelectronics ; Catalysis ; Chemistry Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Nanoplatforms for cancer theranostics
Authors: Daniel M. Packwood | Linh Thi Hoai Nguyen | Pierluigi Cesana | Guoxi Zhang | Aleksandar Staykov | Yasuhide Fukumoto | Đình Hòa Nguyễn
Source: Machine Learning with Applications (article, 2022-02-14)
Field: Materials Science — Materials Chemistry

Machine Learning in Materials Chemistry: An Invitation Materials chemistry is being profoundly influenced by the uptake of machine learning methodologies. Machine learning techniques, in combination with established techniques from computational physics, promise to accelerate the discovery of new materials by elucidating complex structure–property relationships from massive material databases. Despite exciting possibilities, further methodological developments call for a greater synergism between materials chemists, physicists, and engineers on one side, with computer science and math majors on the other. In this review, we provide a non-exhaustive account of machine learning in materials chemistry for computer scientists and applied mathematicians, with an emphasis on molecule datasets and materials chemistry problems. The first part of this review provides a tutorial on how to prepare such datasets for subsequent model building, with an emphasis on the construction of feature vectors. We also provide a self-contained introduction to density functional theory, a method from computational physics which is widely used to generate datasets and compute response variables. The second part reviews two machine learning methodologies which represent the status quo in materials chemistry at present – kernelized machine learning and Bayesian machine learning – and discusses their application to real datasets. In the third part of the review, we introduce some emerging machine learning techniques which have not been widely adopted by materials scientists and therefore present potential avenues for computer science and applied math majors. In the final concluding section, we discuss some recent machine learning-based approaches to real materials discovery problems and speculate on some promising future directions. Artificial intelligence ; Machine learning ; Computer science ; Status quo ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Abir Boublia | Tarek Lemaoui | Jawaher AlYammahi | Ahmad S. Darwish | Akil Ahmad | Manawwer Alam | Fawzi Banat | Yacine Benguerba | Inas M. AlNashef
Source: ACS Sustainable Chemistry & Engineering (article, 2022-12-23)
Field: Materials Science — Biomaterials

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σ<sub>Profiles</sub> Molecular Inputs Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to replace petroleum-based plastics. They are biosynthetizable, biodegradable, and economically viable and have a range of tunable properties. Despite their great potential, the structure and properties of PHA remain unexplored due to their theoretically infinite chemical space. Therefore, computational approaches for accurate predictions of their various properties need to be developed to effectively explore this large chemical space. For this purpose, this work presents a multitask artificial neural network (ANN) capable of predicting the glass transition temperature (Tg) and melting temperature (Tm) of PHA homopolymers and copolymers. The ANN inputs included the σProfiles as molecular parameters describing the monomer chemistry and their composition. In contrast, the polymer molecular weight (M) and polydispersity index (PDI) were used to describe the polymer state. The results showed that after optimizing the hyperparameters, the selected ANN architecture was remarkable in predicting the Tg and Tm of PHA with R2 values of 0.979 and 0.986 and average absolute relative deviation (AARD) of 0.476% and 0.520%, respectively. The proposed model represents an initiative to promote the development of robust, open source, and user-friendly models capable of predicting the properties of polymers based solely on molecular parameters (σProfiles), thereby saving time and resources for researchers worldwide. The framework described in this work is flexible so that it can be applied to a larger chemical space and incorporate other properties of polymers. Polyhydroxyalkanoates ; Chemical space ; Dispersity ; Glass transition ; Polymer ; Biological system ; Materials science ; Artificial neural network ; Monomer ; Computer science ; Biochemical engineering ; Nanotechnology ; Chemistry ; Polymer chemistry ; Artificial intelligence ; Composite material ; Engineering biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Carbon dioxide utilization in catalysis
Authors: Tahani A. Alrebdi | Y.S. Wudil | U.F. Ahmad | Fauziya Yakasai | Jalal Ghanim Mohammed | F.H. Kallas
Source: International Journal of Thermal Sciences (article, 2022-07-12)
Field: Materials Science — Materials Chemistry

Predicting the thermal conductivity of Bi2Te3-based thermoelectric energy materials: A machine learning approach Thermal conductivity ; Materials science ; Support vector machine ; Thermoelectric effect ; Mean squared error ; Linear regression ; Machine learning ; Decision tree ; Thermodynamics ; Computer science ; Mathematics ; Composite material ; Statistics ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Eesha Khare | Chi‐Hua Yu | Constancio González Obeso | Mario Milazzo | David L. Kaplan | Markus J. Buehler
Source: Proceedings of the National Academy of Sciences (article, 2022-09-26)
Field: Materials Science — Biomaterials

Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation Collagen is the most abundant structural protein in humans, providing crucial mechanical properties, including high strength and toughness, in tissues. Collagen-based biomaterials are, therefore, used for tissue repair and regeneration. Utilizing collagen effectively during materials processing ex vivo and subsequent function in vivo requires stability over wide temperature ranges to avoid denaturation and loss of structure, measured as melting temperature (T<sub>m</sub>). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to T<sub>m</sub> values, a robust framework to facilitate the design of collagen sequences with specific T<sub>m</sub> remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific T<sub>m</sub> values. We report 1,000 de novo collagen sequences, and we show that we can efficiently use this model to generate collagen sequences and verify their T<sub>m</sub> values using both experimental and computational methods. We find that the model accurately predicts T<sub>m</sub> values within a few degrees centigrade. Further, using this model, we conduct a high-throughput study to identify the most frequently occurring collagen triplets that can be directly incorporated into collagen. We further discovered that the number of hydrogen bonds within collagen calculated with molecular dynamics (MD) is directly correlated to the experimental measurement of triple-helical quality. Ultimately, we see this work as a critical step to helping researchers develop collagen sequences with specific T<sub>m</sub> values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm. Stability (learning theory) ; Denaturation (fissile materials) ; Computer science ; Algorithm ; Ex vivo ; Toughness ; Materials science ; Biological system ; Chemistry ; In vitro ; Biochemistry ; Machine learning ; Biology ; Composite material Collagen: Extraction and Characterization ; Osteoarthritis Treatment and Mechanisms ; Meat and Animal Product Quality