jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiquan Wei | Shuo Hou | Xin Lin | Shuai Xu | Xiao‐Cheng Dai | Yue‐Hua Li | Jingyu Li | Fang‐Xing Xiao | Yi‐Jun Xu
Source: Journal of the American Chemical Society (article, 2020-12-16)
Field: Materials Science — Materials Chemistry

Unexpected Boosted Solar Water Oxidation by Nonconjugated Polymer-Mediated Tandem Charge Transfer Conjugated polymers are deemed as conductive carrier mediators for engendering the π electrons along the molecular framework, while the role of nonconjugated insulated polymers has been generally overlooked without the capability to participate in the solar-powered oxidation-reduction kinetics and charge-transfer process. Alternatively, considering the ultrashort charge lifetime and significant deficiency of metal nanocluster (NC)-based photosystems, the fine tuning of charge migration over atomically precise ultrasmall metal NCs as novel light-harvesting antennas has so far not yet been unleashed. Here, we unlock the charge-transfer capability of a nonconjugated polymer to modulate the charge flow over metal NCs (Au<sub><i>x</i></sub> and Au<sub>25</sub>) by such a solid-state nonconductive polymer via a conceptually new chemistry strategy by which l-glutathione (GSH)-capped gold (Au<sub><i>x</i></sub>@GSH) NCs and poly(diallyl-dimethylammonium chloride) (PDDA) were alternately self-assembled on the metal oxide (MO: WO<sub>3</sub>, Fe<sub>2</sub>O<sub>3</sub>, and TiO<sub>2</sub>) substrates. The ultrathin nonconjugated PDDA interim layer periodically intercalated in-between Au<sub><i>x</i></sub> (Au<sub>25</sub>) NC layers concurrently serves as an unexpected charge-transfer mediator to foster the unidirectional electron flow from Au<sub><i>x</i></sub>(Au<sub>25</sub>) NCs to MOs by forming a tandem charge-transfer chain, hence endowing the multilayered MO/(PDDA-Au<sub><i>x</i></sub>)<i><sub>n</sub></i> heterostructures with significantly boosted photoelectrochemical water oxidation performance under light irradiation. The unanticipated role of PDDA as a cascade charge mediator is demonstrated to be universal. Our work would unlock the potential charge-transport capability of nonconjugated polymers as a novel charge mediator for solar-to-chemical conversion. Chemistry ; Tandem ; Polymer ; Electron transfer ; Charge (physics) ; Heterojunction ; Photochemistry ; Charge carrier ; Nanotechnology ; Chemical physics ; Optoelectronics ; Materials science ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Advanced Nanomaterials in Catalysis
Authors: Gabriel A. Pinheiro | Johnatan Mucelini | Marinalva Dias Soares | Ronaldo C. Prati | Juarez L. F. Da Silva | Marcos G. Quiles
Source: The Journal of Physical Chemistry A (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset Machine learning (ML) models can potentially accelerate the discovery of tailored materials by learning a function that maps chemical compounds into their respective target properties. In this realm, a crucial step is encoding the molecular systems into the ML model, in which the molecular representation plays a crucial role. Most of the representations are based on the use of atomic coordinates (structure); however, it can increase ML training and predictions' computational cost. Herein, we investigate the impact of choosing free-coordinate descriptors based on the Simplified Molecular Input Line Entry System (SMILES) representation, which can substantially reduce the ML predictions' computational cost. Therefore, we evaluate a feed-forward neural network (FNN) model's prediction performance over five feature selection methods and nine ground-state properties (including energetic, electronic, and thermodynamic properties) from a public data set composed of ∼130k organic molecules. Our best results reached a mean absolute error, close to chemical accuracy, of ∼0.05 eV for the atomization energies (internal energy at 0 K, internal energy at 298.15 K, enthalpy at 298.15 K, and free energy at 298.15 K). Moreover, for the atomization energies, the results obtained an out-of-sample error nine times less than the same FNN model trained with the Coulomb matrix, a traditional coordinate-based descriptor. Furthermore, our results showed how limited the model's accuracy is by employing such low computational cost representation that carries less information about the molecular structure than the most state-of-the-art methods. Representation (politics) ; Artificial neural network ; Computer science ; Matrix (chemical analysis) ; Artificial intelligence ; Function (biology) ; Quantum ; Biological system ; Statistical physics ; Chemistry ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Akansha Dager | Ankur Baliyan | Shunji Kurosu | Toru Maekawa | Masaru Tachibana
Source: Scientific Reports (article, 2020-07-23)
Field: Materials Science — Materials Chemistry

Ultrafast synthesis of carbon quantum dots from fenugreek seeds using microwave plasma enhanced decomposition: application of C-QDs to grow fluorescent protein crystals Herein, we present the rapid synthesis of mono-dispersed carbon quantum dots (C-QDs) via a single-step microwave plasma-enhanced decomposition (MPED) process. Highly-crystalline C-QDs were synthesized in a matter of 5 min using the fenugreek seeds as a sustainable carbon source. It is the first report, to the best of our knowledge, where C-QDs were synthesized using MPED via natural carbon precursor. Synthesis of C-QDs requires no external temperature other than hydrogen (H<sub>2</sub>) plasma. Plasma containing the high-energy electrons and activated hydrogen ions predominantly provide the required energy directly into the reaction volume, thus maximizing the atom economy. C-QDs shows excellent Photoluminescence (PL) activity along with the dual-mode of excitation-dependent PL emission (blue and redshift). We investigate the reason behind the dual-mode of excitation-dependent PL. To prove the efficacy of the MPED process, C-QDs were also derived from fenugreek seeds using the traditional synthesis process, highlighting their respective size-distribution, crystallinity, quantum yield, and PL. Notably, C-QDs synthesis via MPED was 97.2% faster than the traditional thermal decomposition process. To the best of our knowledge, the present methodology to synthesize C-QDs via natural source employing MPED is three times faster and far more energy-efficient than reported so far. Additionally, the application of C-QDs to produce the florescent lysozyme protein crystals "hybrid bio-nano crystals" is also discussed. Such a guest-host strategy can be exploited to develop diverse and complex "bio-nano systems". The florescent lysozyme protein crystals could provide a platform for the development of novel next-generation polychrome luminescent crystals. Quantum dot ; Photoluminescence ; Materials science ; Crystallinity ; Quantum yield ; Nanoparticle ; Fluorescence ; Microwave ; Carbon fibers ; Nanotechnology ; Chemical engineering ; Optoelectronics ; Physics ; Composite number Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Mohammad Tavakkoli Yaraki | Yen Nee Tan
Source: Chemistry - An Asian Journal (review, 2020-08-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metal Nanoparticles‐Enhanced Biosensors: Synthesis, Design and Applications in Fluorescence Enhancement and Surface‐enhanced Raman Scattering Metal nanoparticles (NP) that exhibit localized surface plasmon resonance play an important role in metal-enhanced fluorescence (MEF) and surface-enhanced Raman scattering (SERS). Among the optical biosensors, MEF and SERS stand out to be the most sensitive techniques to detect a wide range of analytes from ions, biomolecules to macromolecules and microorganisms. Particularly, anisotropic metal NPs with strongly enhanced electric field at their sharp corners/edges under a wide range of excitation wavelengths are highly suitable for developing the ultrasensitive plasmon-enhanced biosensors. In this review, we first highlight the reliable methods for the synthesis of anisotropic gold NPs and silver NPs in high yield, as well as their alloys and composites with good control of size and shape. It is followed by the discussion of different sensing mechanisms and recent advances in the MEF and SERS biosensor designs. This includes the review of surface functionalization, bioconjugation and (directed/self) assembly methods as well as the selection/screening of specific biorecognition elements such as aptamers or antibodies for the highly selective bio-detection. The right combinations of metal nanoparticles, biorecognition element and assay design will lead to the successful development of MEF and SERS biosensors targeting different analytes both in-vitro and in-vivo. Finally, the prospects and challenges of metal-enhanced biosensors for future nanomedicine in achieving ultrasensitive and fast medical diagnostics, high-throughput drug discovery as well as effective and reliable theranostic treatment are discussed. Biosensor ; Nanotechnology ; Biomolecule ; Surface plasmon resonance ; Materials science ; Aptamer ; Raman scattering ; Bioconjugation ; Nanoparticle ; Surface modification ; Analyte ; Nanomedicine ; Raman spectroscopy ; Chemistry ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Yanming Zhao | Wenhao Dai | Yun‐Lei Peng | Zheng Niu | Qi Sun | Chuan Shan | Hui Yang | Gaurav Verma | Łukasz Wojtas | Daqiang Yuan | Zhenjie Zhang | Haifeng Dong | Xueji Zhang | Bao Zhang | Yaqing Feng | Shengqian Ma
Source: Angewandte Chemie International Edition (article, 2020-01-08)
Field: Materials Science — Materials Chemistry

A Corrole‐Based Covalent Organic Framework Featuring Desymmetrized Topology Herein, for the first time, we present the successful synthesis of a novel two-dimensional corrole-based covalent organic framework (COF) by reacting the unusual approximately T-shaped 5,10,15-tris(p-aminophenyl)corrole H<sub>3</sub> TPAPC with terephthalaldehyde, which adopts desymmetrized hcb topology and consists of a staggered AB stacking structure with elliptical pores. The resultant corrole-based COF, TPAPC-COF, exhibits high crystallinity and excellent chemical stability. The combination of extended π-conjugated backbone and interlayer noncovalent π-π interactions endows TPAPC-COF with excellent absorption capability in the entire visible-light and even near-infrared regions. Moreover, this work suggests the promise of TPAPC-COF as a new class of photoactive material for efficient singlet-oxygen generation with potential photodynamic therapy application as demonstrated by in vitro anticancer studies. Corrole ; Stacking ; Covalent bond ; Singlet oxygen ; Materials science ; Absorption (acoustics) ; Crystallinity ; Covalent organic framework ; Conjugated system ; Infrared ; Polymer ; Photochemistry ; Nanotechnology ; Topology (electrical circuits) ; Chemistry ; Organic chemistry ; Oxygen ; Physics ; Optics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Porphyrin and Phthalocyanine Chemistry
Authors: Sen Liu | Branden B. Kappes | Behnam Amin-Ahmadi | Othmane Benafan | Zhang Xiao-li | Aaron P. Stebner
Source: Applied Materials Today (article, 2020-12-07)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration Homogenization (climate) ; Shape-memory alloy ; Hysteresis ; Property (philosophy) ; Artificial intelligence ; Process (computing) ; Process design ; Nonlinear system ; Mechanical engineering ; Computer science ; Biological system ; Machine learning ; Materials science ; Process engineering ; Statistical physics ; Physics ; Engineering Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; High Entropy Alloys Studies
Authors: Nils Zimmermann | Anubhav Jain
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity Structure characterization and classification is frequently based on local environment information of all or selected atomic sites in the crystal structure. Therefore, reliable and robust procedures to find coordinated neighbors and to evaluate the resulting coordination pattern (<i>e.g.</i>, tetrahedral, square planar) are critically important for both traditional and machine learning approaches that aim to exploit site or structure information for predicting materials properties. Here, we introduce new local structure order parameters (LoStOPs) that are specifically designed to rapidly detect highly symmetric local coordination environments (<i>e.g.</i>, Platonic solids such as a tetrahedron or an octahedron) as well as less symmetric ones (<i>e.g.</i>, Johnson solids such as a square pyramid). Furthermore, we introduce a Monte Carlo optimization approach to ensure that the different LoStOPs are comparable with each other. We then apply the new local environment descriptors to define site and structure fingerprints and to measure similarity between 61 known coordination environments and 40 commonly studied crystal structures, respectively. After extensive testing and optimization, we determine the most accurate structure similarity assessment procedure to compute all 2.45 billion structure similarities between each pair of the ≈70 000 materials that are currently present in the Materials Project database. Similarity (geometry) ; Crystal structure ; Tetrahedron ; Computer science ; Pyramid (geometry) ; Octahedron ; Square pyramid ; Monte Carlo method ; Crystal structure prediction ; Local structure ; Planar ; Coordination number ; Artificial intelligence ; Data mining ; Algorithm ; Crystallography ; Chemistry ; Mathematics ; Geometry ; Image (mathematics) ; Statistics ; Ion Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Andreas‐David Brunner | Marvin Thielert | Catherine G. Vasilopoulou | Constantin Ammar | Fabian Coscia | Andreas Mund | Ole Hoerning | Nicolai Bache | Amalia Apalategui | Markus Lubeck | Sabrina Richter | David S. Fischer | Oliver Raether | Melvin A. Park | Florian Meier | Fabian J. Theis | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-12-22)
Field: Chemistry — Spectroscopy

Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation Abstract Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing 1–3 . However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary 4,5 . Chemical labeling-based single-cell approaches introduce hundreds of cells into the MS, but direct analysis of single cells has not yet reached the necessary sensitivity, robustness and quantitative accuracy to answer biological questions 6,7 . Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography and a novel trapped ion mobility mass spectrometer, resulting in a more than ten-fold improved sensitivity. We accurately and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators such as CDK2NA, the E2 ubiquitin ligase UBE2S, DNA topoisomerases TOP2A/B and the chromatin regulator HMGA1. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable core proteome despite perturbation, while the transcriptome appears volatile. This emphasizes substantial regulation of translation and sets the stage for its elucidation at the single cell level. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material 8 , posttranslational modifications and small molecule studies to gain unprecedented insights into cellular heterogeneity in health and disease. Proteome ; Transcriptome ; Proteomics ; Computational biology ; Single-cell analysis ; Mass spectrometry ; Biology ; Cell Cycle Protein ; Cell cycle ; Cell ; Chromatin ; Cell biology ; Systems biology ; Chemistry ; Bioinformatics ; DNA ; Biochemistry ; Chromatography ; Gene expression ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Zi‐Jian Li | Yu Ju | Bowen Yu | Xiaoling Wu | Huangjie Lu | Yongxin Li | J. Zhou | Xiaofeng Guo | Zhi‐Hui Zhang | Jian Lin | Jian‐Qiang Wang | Shuao Wang
Source: Chemical Communications (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Modulated synthesis and isoreticular expansion of Th-MOFs with record high pore volume and surface area for iodine adsorption Modulated synthesis and isoreticular expansion of Th-MOFs enable the stepwise construction of hierarchical complexes in which porosity and functionality can be fine-tuned, affording new Th-MOFs with a record high void space (74.0%), the largest surface area (3396.5 m2 g-1) among thorium materials, and excellent adsorption capacities toward radioactive iodine. Adsorption ; Iodine ; Volume (thermodynamics) ; Volume expansion ; Chemical engineering ; Chemistry ; Nanotechnology ; Materials science ; Physical chemistry ; Organic chemistry ; Thermodynamics ; Medicine ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing ; Covalent Organic Framework Applications
Authors: Anthony Cartwright | Kyle Jackson | Christina Morgan | Anne J. Anderson | David W. Britt
Source: Agronomy (review, 2020-07-15)
Field: Materials Science — Materials Chemistry

A Review of Metal and Metal-Oxide Nanoparticle Coating Technologies to Inhibit Agglomeration and Increase Bioactivity for Agricultural Applications Coatings offer a means to control nanoparticle (NP) size, regulate dissolution, and mitigate runoff when added to crops through soil. Simultaneously, coatings can enhance particle binding to plants and provide an additional source of nutrients, making them a valuable component to existing nanoparticle delivery systems. Here, the surface functionalization of metal and metal-oxide nanoparticles to inhibit aggregation and preserve smaller agglomerate sizes for enhanced transport to the rooting zone and improved uptake in plants is reviewed. Coatings are classified by type and by their efficacy to mitigate agglomeration in soils with variable pH, ionic concentration, and natural organic matter profiles. Varying degrees of success have been reported using a range of different polymers, biomolecules, and inorganic surface coatings. Advances in zwitterionic coatings show the best results for maintaining nanoparticle stability in solutions even under high salinity and temperature conditions, whereas coating by the soil component humic acid may show additional benefits such as promoting dissolution and enhancing bioavailability in soils. Pre-tuning of NP surface properties through exposure to select natural organic matter, microbial products, and other biopolymers may yield more cost-effective nonagglomerating metal/metal-oxide NPs for soil applications in agriculture. Dissolution ; Nanoparticle ; Surface modification ; Coating ; Chemical engineering ; Materials science ; Oxide ; Economies of agglomeration ; Bioavailability ; Metal ; Soil water ; Nanotechnology ; Environmental chemistry ; Chemistry ; Environmental science ; Metallurgy ; Soil science Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Polymer-Based Agricultural Enhancements
Authors: David Balcells | Bastian Bjerkem Skjelstad
Source: Journal of Chemical Information and Modeling (article, 2020-11-09)
Field: Materials Science — Materials Chemistry

tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal-organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, -1}<i>e</i>. The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm. HOMO/LUMO ; Molecular orbital ; Chemistry ; Transition metal ; Computational chemistry ; Chemical physics ; Natural bond orbital ; Molecule ; Density functional theory ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics