jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuanyu Huang | Xiaojian Xiang | Jinhui Nie | Deli Peng | Fuwei Yang | Zhanghui Wu | Haiyang Jiang | Zhiping Xu | Quanshui Zheng
Source: Nature Communications (article, 2021-04-15)
Field: Materials Science — Materials Chemistry

Microscale Schottky superlubric generator with high direct-current density and ultralong life Miniaturized or microscale generators that can effectively convert weak and random mechanical energy into electricity have significant potential to provide solutions for the power supply problem of distributed devices. However, owing to the common occurrence of friction and wear, all such generators developed so far have failed to simultaneously achieve sufficiently high current density and sufficiently long lifetime, which are crucial for real-world applications. To address this issue, we invent a microscale Schottky superlubric generator (S-SLG), such that the sliding contact between microsized graphite flakes and n-type silicon is in a structural superlubric state (an ultra-low friction and wearless state). The S-SLG not only generates high current (~210 Am<sup>-2</sup>) and power (~7 Wm<sup>-2</sup>) densities, but also achieves a long lifetime of at least 5,000 cycles, while maintaining stable high electrical current density (~119 Am<sup>-2</sup>). No current decay and wear are observed during the experiment, indicating that the actual persistence of the S-SLG is enduring or virtually unlimited. By excluding the mechanism of friction-induced excitation in the S-SLG, we further demonstrate an electronic drift process during relative sliding using a quasi-static semiconductor finite element simulation. Our work may guide and accelerate the future use of S-SLGs in real-world applications. Microscale chemistry ; Generator (circuit theory) ; Current density ; Current (fluid) ; Materials science ; Nanotechnology ; Physics ; Electrical engineering ; Power (physics) ; Engineering Diamond and Carbon-based Materials Research ; Nanowire Synthesis and Applications ; Mechanical and Optical Resonators
Authors: Meng Jiang | Yue Song | Mukesh Kumar Kanwar | Golam Jalal Ahammed | Shujun Shao | Jie Zhou
Source: Journal of Nanobiotechnology (review, 2021-12-01)
Field: Materials Science — Materials Chemistry

Phytonanotechnology applications in modern agriculture With the rapidly changing global climate, the agricultural systems are confronted with more unpredictable and harsh environmental conditions than before which lead to compromised food production. Thus, to ensure safer and sustainable crop production, the use of advanced nanotechnological approaches in plants (phytonanotechnology) is of great significance. In this review, we summarize recent advances in phytonanotechnology in agricultural systems that can assist to meet ever-growing demands of food sustainability. The application of phytonanotechnology can change traditional agricultural systems, allowing the target-specific delivery of biomolecules (such as nucleotides and proteins) and cater the organized release of agrochemicals (such as pesticides and fertilizers). An amended comprehension of the communications between crops and nanoparticles (NPs) can improve the production of crops by enhancing tolerance towards environmental stresses and optimizing the utilization of nutrients. Besides, approaches like nanoliposomes, nanoemulsions, edible coatings, and other kinds of NPs offer numerous selections in the postharvest preservation of crops for minimizing food spoilage and thus establishing phtonanotechnology as a sustainable tool to architect modern agricultural practices. Agrochemical ; Agriculture ; Sustainability ; Crop protection ; Agricultural productivity ; Sustainable agriculture ; Biotechnology ; Business ; SAFER ; Computer science ; Environmental science ; Agroforestry ; Biology ; Ecology Nanoparticles: synthesis and applications ; Plant tissue culture and regeneration ; Seed Germination and Physiology
Authors: Chia‐Feng Tsai | Pengfei Zhang | David Scholten | Kendall Martin | Yi-Ting Wang | Rui Zhao | William Chrisler | Dhwani Patel | Maowei Dou | Yuzhi Jia | Carolina Reduzzi | Xia Liu | Ronald Moore | Kristin Burnum-Johnson | Miao‐Hsia Lin | Chuan‐Chih Hsu | Jon Jacobs | Jacob Kagan | Sudhir Srivastava | Karin Rodland | H Wiley | Weijun Qian | Richard Smith | Ying Zhu | Massimo Cristofanilli | Tao Liu | Huiping Liu | Tujin Shi
Source: Communications Biology (article, 2021-03-01)
Field: Chemistry — Spectroscopy

Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for 'all-in-one' one-pot sample preparation. This 'all-in-one' method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics. Proteomics ; Proteome ; Cell ; Pulmonary surfactant ; Chemistry ; Circulating tumor cell ; Single-cell analysis ; Quantitative proteomics ; Sample preparation ; Chromatography ; Metastasis ; Biology ; Biochemistry ; Cancer Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biosensors and Analytical Detection
Authors: Puneet Mishra | Douglas N. Rutledge | Jean‐Michel Roger | Khan Wali | Haris Ahmad Khan
Source: Talanta (article, 2021-03-11)
Field: Chemistry — Analytical Chemistry

Chemometric pre-processing can negatively affect the performance of near-infrared spectroscopy models for fruit quality prediction Chemometrics pre-processing of spectral data is widely performed to enhance the predictive performance of near-infrared (NIR) models related to fresh fruit quality. Pre-processing approaches in the domain of NIR data analysis are used to remove the scattering effects, thus, enhancing the absorption components related to the chemical properties. However, in the case of fresh fruit, both the scattering and absorption properties are of key interest as they jointly explain the physicochemical state of a fruit. Therefore, pre-processing data that reduces the scattering information in the spectra may lead to poorly performing models. The objectives of this study are to test two hypotheses to explore the effect of pre-processing on NIR spectra of fresh fruit. The first hypothesis is that the pre-processing of NIR spectra with scatter correction techniques can reduce the predictive performance of models as the scatter correction can reduce the useful scattering information correlated to the property of interest. The second hypothesis is that the Deep Learning (DL) can model the raw absorbance data (mix of scattering and absorption) much more efficiently than the Partial Least Squares (PLS) regression analysis. To test the hypotheses, a real NIR data set related to dry matter (DM) prediction in mango fruit was used. The dataset consisted of a total of 11,420 NIR spectra and reference DM measurements for model training and independent testing. The chemometric pre-processing methods explored were standard normal variate (SNV), variable sorting for normalization (VSN), Savitzky-Golay based 2nd derivative and their combinations. Further two modelling approaches i.e., PLS regression and DL were used to evaluate the effect of pre-processing. The results showed that the best root mean squared error of prediction (RMSEP) for both the PLS and DL models were obtained with the raw absorbance data. The spectral pre-processing in general decreased the performance of both the PLS and DL models. Further, the DL model attained the lowest RMSEP of 0.76%, which was 13% lower compared to the PLS regression on the raw absorbance data. Pre-processing approaches should be carefully used while analysing the NIR data related to fresh fruit. Partial least squares regression ; Chemistry ; Chemometrics ; Biological system ; Normalization (sociology) ; Near-infrared spectroscopy ; Scattering ; Absorbance ; Spectroscopy ; Pattern recognition (psychology) ; Artificial intelligence ; Statistics ; Mathematics ; Optics ; Computer science ; Chromatography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Aya M. Mostafa | Stephen Barton | Stephen P. Wren | James Barker
Source: TrAC Trends in Analytical Chemistry (article, 2021-09-08)
Field: Chemistry — Analytical Chemistry

Review on molecularly imprinted polymers with a focus on their application to the analysis of protein biomarkers Molecularly imprinted polymer ; Nanotechnology ; Computer science ; Polymer ; Molecular imprinting ; Combinatorial chemistry ; Chemistry ; Materials science ; Selectivity ; Organic chemistry Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Microfluidic and Capillary Electrophoresis Applications
Authors: Aastha Mahapatra | Tanya Prasad | Tripti Sharma
Source: Future Journal of Pharmaceutical Sciences (review, 2021-06-19)
Field: Chemistry — Organic Chemistry

Pyrimidine: a review on anticancer activity with key emphasis on SAR Abstract Background Cancer is a global health challenge, it impacts the quality of life and its treatment is associated with several side effects. Resistance of the cancer cells to the existing drugs has led to search for novel anticancer agents. Pyrimidine, a privileged scaffold, is part of living organisms and plays vital role in various biological procedures as well as in cancer pathogenesis. Due to resemblance in structure with the nucleotide base pair of DNA and RNA, it is recognized as valuable compound in the treatment of cancer. Main text Many novel pyrimidine derivatives have been designed and developed for their anticancer activity in the last few years. The present review aims to focus on the structure activity relationship (SAR) of pyrimidine derivatives as anticancer agent from the last decade. Conclusion This review intends to assist in the development of more potent and efficacious anticancer drugs with pyrimidine scaffold. Graphical abstract Pyrimidine ; Cancer ; Computational biology ; Medicine ; Chemistry ; Biology ; Biochemistry ; Internal medicine Synthesis and biological activity ; Biochemical and Molecular Research ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Minyi Dai | Mehmet F. Demirel | Yingyu Liang | Jia-Mian Hu
Source: npj Computational Materials (article, 2021-07-09)
Field: Materials Science — Materials Chemistry

Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials Abstract Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embedding of polycrystalline microstructure which incorporates not only the physical features of individual grains but also their interactions. The embedding is then linked to the target property using a feed-forward neural network. Using the magnetostriction of polycrystalline Tb 0.3 Dy 0.7 Fe 2 alloys as an example, we show that a single GNN model with fixed network architecture and hyperparameters allows for a low prediction error of ~10% over a group of remarkably different microstructures as well as quantifying the importance of each feature in each grain of a microstructure to its magnetostriction. Such a microstructure-graph-based GNN model, therefore, enables an accurate and interpretable prediction of the properties of polycrystalline materials. Crystallite ; Artificial neural network ; Embedding ; Microstructure ; Feature (linguistics) ; Graph ; Pattern recognition (psychology) Machine Learning in Materials Science ; Magnetic Properties and Applications ; Model Reduction and Neural Networks
Authors: R.I. Zubatyuk | Justin S. Smith | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev
Source: Nature Communications (article, 2021-08-11)
Field: Materials Science — Materials Chemistry

Teaching a neural network to attach and detach electrons from molecules Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2-3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions. Electronegativity ; Open shell ; Density functional theory ; Artificial neural network ; Molecule ; Electron ; Ionization ; Electrophile ; Computer science ; Ion ; Physics ; Computational chemistry ; Chemical physics ; Statistical physics ; Chemistry ; Atomic physics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Jun Liu | Yingyan Zhang | Yihe Zhang | S. Kitipornchai | Jie Yang
Source: Materials & Design (article, 2021-12-28)
Field: Materials Science — Materials Chemistry

Machine learning assisted prediction of mechanical properties of graphene/aluminium nanocomposite based on molecular dynamics simulation Predicting mechanical properties of graphene-reinforced metal matrix nanocomposites (GRMMNCs) usually requires atomistic simulations that are computationally expensive without scalability or micromechanics-based models (such as widely used Halpin-Tsai model) that may lead to considerable errors in many cases. This paper first combines molecular dynamic (MD) simulation and machine learning (ML) techniques to predict the mechanical properties of graphene reinforced aluminium (Gr/Al) nanocomposites and use them to modify Halpin-Tsai model. Extensive MD results for Young’s modulus and ultimate tensile strength of Gr/Al nanocomposites are obtained, with the intricate effects of graphene’s volume fraction, alignment angle, chirality and environment temperature being taken into account. After training and optimization based on MD data, ML models are developed with the capability in estimating Young’s modulus and ultimate tensile strength. The micromechanics based Halpin-Tsai model is then modified by using the Young’s modulus predicted by both MD and ML models such that the Young’s modulus can be readily determined with significantly improved accuracy from an explicit relationship that is very easy to use in the analysis and engineering design of Gr/Al nanocomposite structures. Materials science ; Graphene ; Nanocomposite ; Aluminium ; Molecular dynamics ; Nanotechnology ; Composite material Carbon Nanotubes in Composites ; Aluminum Alloys Composites Properties ; Boron and Carbon Nanomaterials Research
Authors: Igor Poltavsky | Alexandre Tkatchenko
Source: The Journal of Physical Chemistry Letters (article, 2021-07-09)
Field: Materials Science — Materials Chemistry

Machine Learning Force Fields: Recent Advances and Remaining Challenges In chemistry and physics, machine learning (ML) methods promise transformative impacts by advancing modeling and improving our understanding of complex molecules and materials. Each ML method comprises a mathematically well-defined procedure, and an increasingly larger number of easy-to-use ML packages for modeling atomistic systems are becoming available. In this Perspective, we discuss the general aspects of ML techniques in the context of creating ML force fields. We describe common features of ML modeling and quantum-mechanical approximations, so-called global and local ML models, and the physical differences behind these two classes of approaches. Finally, we describe the recent developments and emerging directions in the field of ML-driven molecular modeling. This Perspective aims to inspire interdisciplinary collaborations crossing the borders between physical chemistry, chemical physics, computer science, and data science. Transformative learning ; Perspective (graphical) ; Context (archaeology) ; Field (mathematics) ; Computer science ; Nanotechnology ; Data science ; Management science ; Artificial intelligence ; Engineering ; Mathematics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yang Wang | Hao Lv | Erik Svensson Grape | Carlo Alberto Gaggioli | Akhil Tayal | Aditya Dharanipragada | Tom Willhammar | A. Ken Inge | Xiaodong Zou | Ben Liu | Zhehao Huang
Source: Journal of the American Chemical Society (article, 2021-04-26)
Field: Chemistry — Inorganic Chemistry

A Tunable Multivariate Metal–Organic Framework as a Platform for Designing Photocatalysts Catalysts for photochemical reactions underlie many foundations in our lives, from natural light harvesting to modern energy storage and conversion, including processes such as water photolysis by TiO<sub>2</sub>. Recently, metal-organic frameworks (MOFs) have attracted large interest within the chemical research community, as their structural variety and tunability yield advantages in designing photocatalysts to address energy and environmental challenges. Here, we report a series of novel multivariate metal-organic frameworks (MTV-MOFs), denoted as MTV-MIL-100. They are constructed by linking aromatic carboxylates and AB<sub>2</sub>OX<sub>3</sub> bimetallic clusters, which have ordered atomic arrangements. Synthesized through a solvent-assisted approach, these ordered and multivariate metal clusters offer an opportunity to enhance and fine-tune the electronic structures of the crystalline materials. Moreover, mass transport is improved by taking advantage of the high porosity of the MOF structure. Combining these key advantages, MTV-MIL-100(Ti,Co) exhibits a high photoactivity with a turnover frequency of 113.7 mol<sub>H2</sub> g<sub>cat.</sub><sup>-1</sup> min<sup>-1</sup>, a quantum efficiency of 4.25%, and a space time yield of 4.96 × 10<sup>-5</sup> in the photocatalytic hydrolysis of ammonia borane. Bridging the fields of perovskites and MOFs, this work provides a novel platform for the design of highly active photocatalysts. Chemistry ; Ammonia borane ; Metal-organic framework ; Bimetallic strip ; Photocatalysis ; Nanotechnology ; Quantum yield ; Catalysis ; Porosity ; Yield (engineering) ; Chemical engineering ; Photochemistry ; Organic chemistry ; Dehydrogenation ; Materials science ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Matthew J. Chalkley | Samuel I. Mann | William F. DeGrado
Source: Nature Reviews Chemistry (review, 2021-12-06)
Field: Chemistry — Inorganic Chemistry

De novo metalloprotein design Metalloprotein ; Protein design ; Function (biology) ; Ligand (biochemistry) ; Protein structure ; Chemistry ; Rational design ; Active site ; Computational biology ; Enzyme ; Nanotechnology ; Biochemistry ; Biology ; Materials science ; Receptor ; Genetics Metal-Catalyzed Oxygenation Mechanisms ; Metal complexes synthesis and properties ; Metalloenzymes and iron-sulfur proteins
Authors: Yuezhou Zhang | Huaguang Cui | Ruiqi Zhang | Hongbo Zhang | Wei Huang
Source: Advanced Science (review, 2021-07-29)
Field: Materials Science — Biomaterials

Nanoparticulation of Prodrug into Medicines for Cancer Therapy This article provides a broad spectrum about the nanoprodrug fabrication advances co-driven by prodrug and nanotechnology development to potentiate cancer treatment. The nanoprodrug inherits the features of both prodrug concept and nanomedicine know-how, attempts to solve underexploited challenge in cancer treatment cooperatively. Prodrugs can release bioactive drugs on-demand at specific sites to reduce systemic toxicity, this is done by using the special properties of the tumor microenvironment, such as pH value, glutathione concentration, and specific overexpressed enzymes; or by using exogenous stimulation, such as light, heat, and ultrasound. The nanotechnology, manipulating the matter within nanoscale, has high relevance to certain biological conditions, and has been widely utilized in cancer therapy. Together, the marriage of prodrug strategy which shield the side effects of parent drug and nanotechnology with pinpoint delivery capability has conceived highly camouflaged Trojan horse to maneuver cancerous threats. Prodrug ; Nanomedicine ; Cancer therapy ; Nanotechnology ; Cancer treatment ; Cancer ; Pharmacology ; Broad spectrum ; Medicine ; Chemistry ; Materials science ; Nanoparticle ; Combinatorial chemistry ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications