jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiye Son | Daniela Kalafatović | Mohit Kumar | Barney Yoo | Mike Cornejo | Marı́a Contel | Rein V. Ulijn
Source: ACS Nano (article, 2019-01-29)
Field: Materials Science — Biomaterials

Customizing Morphology, Size, and Response Kinetics of Matrix Metalloproteinase-Responsive Nanostructures by Systematic Peptide Design Overexpression and activation of matrix metalloproteinase-9 (MMP-9) is associated with multiple diseases and can serve as a stimulus to activate nanomaterials for sensing and controlled release. In order to achieve autonomous therapeutics with improved space-time targeting capabilities, several features need to be considered beyond the introduction of an enzyme-cleavable linker into a nanostructure. We introduce guiding principles for a customizable platform using supramolecular peptide nanostructures with three modular components to achieve tunable kinetics and morphology changes upon MMP-9 exposure. This approach enables (1) fine-tuning of kinetics through introduction of ordered/disordered structures, (2) a 12-fold variation in hydrolysis rates achieved by electrostatic (mis)matching of particle and enzyme charge, and (3) selection of enzymatic reaction products that are either cell-killing nanofibers or disintegrate. These guiding principles, which can be rationalized and involve exchange of just a few amino acids, enable systematic customization of enzyme-responsive peptide nanostructures for general use in performance optimization of enzyme-responsive materials. Nanotechnology ; Nanostructure ; Peptide ; Materials science ; Nanomaterials ; Kinetics ; Enzyme kinetics ; Linker ; Nanofiber ; Supramolecular chemistry ; Biophysics ; Enzyme ; Chemistry ; Computer science ; Biochemistry ; Biology ; Physics ; Active site Supramolecular Self-Assembly in Materials ; Peptidase Inhibition and Analysis ; Advanced biosensing and bioanalysis techniques
Authors: Jonathan W. Lehmann | Ian T. Crouch | Daniel J. Blair | Melanie Trobe | Pulin Wang | Junqi Li | Martin D. Burke
Source: Nature Communications (article, 2019-03-20)
Field: Chemistry — Organic Chemistry

Axial shielding of Pd(II) complexes enables perfect stereoretention in Suzuki-Miyaura cross-coupling of Csp3 boronic acids Stereocontrolled Csp<sup>3</sup> cross-coupling can fundamentally change the types of chemical structures that can be mined for molecular functions. Although considerable progress in achieving the targeted chemical reactivity has been made, controlling stereochemistry in Csp<sup>3</sup> cross-coupling remains challenging. Here we report that ligand-based axial shielding of Pd(II) complexes enables Suzuki-Miyaura cross-coupling of unactivated Csp<sup>3</sup> boronic acids with perfect stereoretention. This approach leverages key differences in spatial orientation between competing pathways for stereoretentive and stereoinvertive transmetalation of Csp<sup>3</sup> boronic acids to Pd(II). We show that axial shielding enables perfectly stereoretentive cross-coupling with a range of unactivated secondary Csp<sup>3</sup> boronic acids, as well as the stereocontrolled synthesis of xylarinic acid B and all of its Csp<sup>3</sup> stereoisomers. We expect these ligand design principles will broadly enable the continued search for practical and effective methods for stereospecific Csp<sup>3</sup> cross-coupling. Transmetalation ; Boronic acid ; Electromagnetic shielding ; Ligand (biochemistry) ; Coupling (piping) ; Chemistry ; Combinatorial chemistry ; Reactivity (psychology) ; Stereospecificity ; Design elements and principles ; Stereochemistry ; Organic chemistry ; Materials science ; Computer science ; Catalysis ; Biochemistry Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry ; Coordination Chemistry and Organometallics
Authors: Soumya Mukherjee | Shivani Sharma | Sujit K. Ghosh
Source: APL Materials (article, 2019-05-01)
Field: Chemistry — Inorganic Chemistry

Hydrophobic metal-organic frameworks: Potential toward emerging applications Metal-organic frameworks, popularly known as MOFs, have come of age—thanks to custom-built design principles enabling them to serve a myriad of task-specific applications. In an era where &amp;gt;6000 new MOFs are annually published, driven by academic research, a new generation of MOFs that can find use in niche markets is on the rise. One of the foremost challenges that plague the translation of MOFs into materials suited for commercialization is their water-influenced degradation, largely accountable to hydrolytic instability and the ubiquity of water. Hydrophobic MOFs offer one of the most promising solutions which can enable these porous materials to leverage their functionalization guided properties even under extreme humid conditions, a criterion that application-ready materials should necessarily serve. In this contribution, our discussion focuses on the state-of-the-art hydrophobic MOFs and MOF-derived composites. Outlining a brief overview of the structure-property correlation aspects in each of these promising hydrophobic MOFs with little or no influence exhibited to water and/or humidity, a future outlook is put forward to enable potentially better design strategies leading to futuristic MOFs tolerant to water/moisture. Concise premise of this review allows us to limit our discussion to the design principles in action behind the most hydrophobic MOFs/derived composites reported thus far and to discuss their prime applications viz., oil/water separation inclusive of self-cleaning, hydrocarbon separation, and tackling marine oil spillage. When synergized, such improved porous material design approaches lean toward a greener environment of tomorrow. Metal-organic framework ; Materials science ; Nanotechnology ; Organic chemistry ; Adsorption ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Enhanced Oil Recovery Techniques
Authors: Long Pan | Zilu Liu | Mi Tian | Bob C. Schroeder | Abil E. Aliev | Charl F. J. Faul
Source: ACS Applied Materials & Interfaces (article, 2019-12-02)
Field: Materials Science — Materials Chemistry

Luminescent and Swellable Conjugated Microporous Polymers for Detecting Nitroaromatic Explosives and Removing Harmful Organic Vapors Four new conjugated microporous polymers (CMPs) were synthesized by a Buchwald-Hartwig (BH) cross-coupling reaction of tri- and tetrafunctionalized precursors to yield materials with tunable surface area and pore size distribution. This approach yielded <b>LPCMP1-4</b>, CMPs with significantly higher Brunauer-Emmett-Teller (BET) surface areas (more than 5 times higher) than other related BH-based CMPs. These CMPs possess not only high BET specific surface areas and high chemical and thermal stabilities, but also exhibit outstanding swellability. To the best of our knowledge, swellable behavior was studied in great detail for CMPs for the first time, with the greatest degree of swelling for methanol reaching 16.5 and 16.3 mL g<sup>-1</sup> for <b>LPCMP1</b> and <b>LPCMP3</b>, respectively. Owing to their excellent swellability, we further studied the adsorption capacity of these CMPs for different toxic organic vapors (including toluene and methanol). <b>LPCMP1</b> and <b>LPCMP3</b> adsorbed 124 and 117 mg g<sup>-1</sup> toluene, respectively, at saturated vapor pressure. For methanol, the adsorption capacities of <b>LPCMP1</b> and <b>LPCMP3</b> were up to 250 and 215 mg g<sup>-1</sup>, respectively, which are the highest recorded values when compared with published data for CMPs, HCPs, MOFs, and porous carbons. These materials are promising candidates for the removal and elimination of hazardous organic vapors and chemical warfare agents. Moreover, all the polymers show high sensitivity to nitroaromatic explosives. <b>LPCMP2</b> and <b>LPCMP4</b> exhibit high selectivity for TNT and may be suitable as new candidates to selectively detect TNT for security or environmental applications. Conjugated microporous polymer ; Materials science ; Microporous material ; Explosive material ; Polymer ; Conjugated system ; Luminescence ; Explosive detection ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Optoelectronics ; Composite material Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Euiyeon Lee | Hyunjin Jeon | Minhyeong Lee | Jeahee Ryu | Chungwon Kang | Soyoun Kim | Junghyun Jung | Youngeun Kwon
Source: Scientific Reports (article, 2019-02-21)
Field: Materials Science — Materials Chemistry

Molecular origin of AuNPs-induced cytotoxicity and mechanistic study Gold nanoparticles (AuNPs) with diverse physicochemical properties are reported to affect biological systems differently, but the relationship between the physicochemical properties of AuNPs and their biological effects is not clearly understood. Here, we aimed to elucidate the molecular origins of AuNP-induced cytotoxicity and their mechanisms, focusing on the surface charge and structural properties of modified AuNPs. We prepared a library of well-tailored AuNPs exhibiting various functional groups and surface charges. Through this work, we revealed that the direction or the magnitude of surface charge is not an exclusive factor that determines the cytotoxicity of AuNPs. We, instead, suggested that toxic AuNPs share a common structural characteristics of a hydrophobic moiety neighbouring the positive charge, which can induce lytic interaction with plasma membrane. Mechanistic study showed that the toxic AuNPs interfered with the formation of cytoskeletal structure to slow cell migration, inhibited DNA replication and caused DNA damage via oxidative stress to hinder cell proliferation. Gene expression analysis showed that the toxic AuNPs down-regulated genes associated with cell cycle processes. We discovered structural characteristics that define the cytotoxic AuNPs and suggested the mechanisms of their cytotoxicity. These findings will help us to understand and to predict the biological effects of modified AuNPs based on their physicochemical properties. Cytotoxicity ; Colloidal gold ; DNA damage ; Lytic cycle ; Chemistry ; Biophysics ; DNA ; Surface charge ; Cell ; DNA replication ; Nanotechnology ; Nanoparticle ; Biochemistry ; Biology ; Materials science ; In vitro ; Genetics Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Tahar Ayad | Aurélie Gernet | Jean‐Luc Pirat | David Virieux
Source: Tetrahedron (article, 2019-06-29)
Field: Chemistry — Organic Chemistry

Enantioselective reactions catalyzed by phosphine oxides Enantioselective synthesis ; Lewis acids and bases ; Phosphine ; Catalysis ; Chemistry ; Phosphine oxide ; Frustrated Lewis pair ; Organic molecules ; Subject matter ; Organic chemistry ; Organic synthesis ; Organocatalysis ; Combinatorial chemistry ; Stereoselectivity ; Molecule ; Political science Asymmetric Synthesis and Catalysis ; Chemical Synthesis and Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Musa Yahaya Pudza | Zurina Zainal Abidin | Suraya Abdul Rashid | Faizah Md Yasin | A. S. M. Noor | Mohammed Issa
Source: Processes (article, 2019-10-05)
Field: Materials Science — Materials Chemistry

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg–Marquardt back propagation (LMBP) algorithm. Considering R2, root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer size. The best hidden layer of neurons were discovered at point 4-8, to confirm the validity of carbon dots, characterization of surface morphology and particles sizes of CDs were conducted with favorable confirmations of the unique characteristics and attributes of synthesized CDs by hydrothermal route. Mean squared error ; Response surface methodology ; Artificial neural network ; Computer science ; Carbon fibers ; Process (computing) ; Biological system ; Process engineering ; Nanotechnology ; Algorithm ; Materials science ; Mathematics ; Artificial intelligence ; Machine learning ; Engineering ; Statistics ; Composite number Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Electrochemical sensors and biosensors
Authors: Jon Paul Janet | Sahasrajit Ramesh | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2019-12-20)
Field: Materials Science — Materials Chemistry

Accurate Multi-Objective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization &lt;p&gt;The accelerated discovery of materials for real world applications requires the achievement of multiple design objectives. Satisfaction of such constraints requires exploration of multi-million compound libraries over which even density-functional theory (DFT) screening is intractable. Machine learning (ML, e.g., artificial neural network, ANN, or Gaussian process, GP) models for this task are limited by training data availability and predictive uncertainty quantification (UQ). We overcome such limitations by using efficient global optimization (EGO) with the multi-dimensional expected improvement (EI) criterion. EGO balances exploitation of a trained model with acquisition of new DFT data at the Pareto front, the region of chemical space that contains the optimal trade-off between multiple design criteria. We demonstrate this approach for the simultaneous optimization of redox potential and solubility in candidate M(II)/M(III) redox couples for redox flow batteries from a space of 2.8M transition metal complexes designed for stability in practical RFB applications. We employ latent-distance-based UQ with a multi-task ANN to enable model generalization that surpasses that of a GP. With this approach, ANN prediction and EI scoring of the full 2.8M complex space is achieved in minutes. Starting from ca. 100 representative points, EGO improves both properties by 3-4 standard deviations in only five generations. Analysis of lookahead errors confirms rapid ANN model improvement during the EGO process, achieving suitable accuracy for predictive design in the space of transition metal complexes. The ANN-driven EI approach achieves at least 500-fold acceleration over random search, identifying a Pareto-optimal design in around five weeks instead of fifty years.&lt;br&gt;&lt;/p&gt; Artificial neural network ; Computer science ; Chemical space ; Generalization ; Artificial intelligence ; Mathematical optimization ; Machine learning ; Mathematics ; Chemistry Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Xiaowei Song | Jiuming He | Xuechao Pang | Jin Zhang | Chenglong Sun | Luojiao Huang | Chao Li | Qingce Zang | Xin Li | Zhigang Luo | Ruiping Zhang | Ping Xie | Xiaoyu Liu | Yan Li | Xiaoguang Chen | Zeper Abliz
Source: Analytical Chemistry (article, 2019-01-14)
Field: Chemistry — Spectroscopy

Virtual Calibration Quantitative Mass Spectrometry Imaging for Accurately Mapping Analytes across Heterogenous Biotissue It is highly challenging to quantitatively map multiple analytes in biotissues without specific chemical labeling. Quantitative mass spectrometry imaging (QMSI) has this potential but still poses technical issues for its variant ionization efficiency across a complicated, heterogeneous biomatrices. Herein, a self-developed air-flow-assisted desorption electrospray ionization (AFADESI) is introduced to present a proof of concept method, virtual calibration (VC) QMSI. This method screens and utilizes analyte response-related endogenous metabolite ions from each mass spectrum as native internal standards (IS). Through machine-learning-based regression and clustering, tissue-specific ionization variation can be automatically recognized, predicted, and normalized region by region or pixel by pixel. Therefore, the quantity of analytes can be accurately mapped across highly structural biosamples including whole body, kidney, brain, tumor, etc. VC-QMSI has the advantages of simple sample preparation without laborious isotopic IS synthesis, extrapolation for those unknown tissues or regions without previous investigation, and automatic spatial recognition without histological guidance. This strategy is suitable for mass spectrometry imaging using a variety of in situ ionization techniques. It is believed that VC-QMSI has wide applicability for drug candidate's discovery, molecular mechanism elucidation, biomarker validation, and clinical diagnosis. Chemistry ; Analyte ; Mass spectrometry imaging ; Mass spectrometry ; Calibration ; Desorption electrospray ionization ; Sample preparation ; Electrospray ionization ; Ionization ; Analytical Chemistry (journal) ; Chromatography ; Chemical ionization ; Ion Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Charles Le Losq | Andrew J. Berry | Mark A. Kendrick | Daniel R. Neuville | Hugh O’Neill
Source: American Mineralogist (article, 2019-04-11)
Field: Materials Science — Ceramics and Composites

Determination of the oxidation state of iron in Mid-Ocean Ridge basalt glasses by Raman spectroscopy Abstract A series of synthetic Mid-Ocean Ridge Basalt (MORB) glasses with Fe3+/FeTOT from 0 to 1, determined previously by Mössbauer spectroscopy, were used to test methods for quantifying Fe3+/FeTOT by Raman spectroscopy. Six numerical data reduction methods were investigated, based on conventional approaches as well as supervised and unsupervised machine learning algorithms. For the set of MORB glass standards, with fixed composition, the precision of all methods was ≤±0.04 (1 St.dev.). However, Raman spectra recorded for 42 natural MORB glasses from a wide range of locations revealed a strong correlation between the spectra and composition, despite the latter varying only over a relatively limited range, such that the methods calibrated using the glass standards are not directly applicable to the natural samples. This compositional effect can be corrected by using a compositional term that links spectral variations to the Fe3+/FeTOT value of the glass. The resulting average Fe3+/FeTOT determined by Raman spectroscopy was 0.090 ± 0.067 (n = 42). This value agrees with the latest Fe K-edge XANES and wet-chemistry estimates of 0.10 ± 0.02. The larger uncertainty of the Raman determination reflects the sensitivity of Raman spectroscopy to small changes in the glass structure. While this sensitivity is detrimental for high precision Fe3+/FeTOT determinations, it allows the major element composition of natural MORB glasses to be determined within 1 mol% through the use of an artificial neural network. This suggests that Raman spectrometers may be used to determine the composition of samples in situ at difficult to access locations that are incompatible with X-ray spectrometry (e.g., mid-ocean ridges). Raman spectroscopy ; Basalt ; Spectroscopy ; Analytical Chemistry (journal) ; Oxidation state ; Ridge ; Materials science ; Spectral line ; Geology ; Mineralogy ; Chemistry ; Optics ; Geochemistry ; Physics ; Metallurgy ; Environmental chemistry ; Metal Glass properties and applications ; Geological and Geochemical Analysis ; Mineralogy and Gemology Studies
Authors: Arindam Paul | Pınar Acar | Wei‐keng Liao | Alok Choudhary | Veera Sundararaghavan | Ankit Agrawal
Source: Computational Materials Science (article, 2019-01-27)
Field: Materials Science — Materials Chemistry

Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation Microstructure ; Computer science ; Machining ; Sampling (signal processing) ; Function (biology) ; Optimization problem ; Materials science ; Algorithm ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Yuta Suzuki | Hideitsu Hino | Masato Kotsugi | Kanta Ono
Source: npj Computational Materials (article, 2019-03-29)
Field: Materials Science — Materials Chemistry

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures Abstract Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade. One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments, which has given rise to the need for accelerated and accurate automated estimation of the properties of materials. In this regard, spectroscopic data are widely used for materials discovery because these data include essential information about materials. An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure, or kernel function. The required measure should be robust in terms of peak shifting, peak broadening, and noise. However, the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved. We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra. The similarity measures show good correspondence with the materials parameter, that is, the crystal-field parameter, in all measures. The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening. We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra. The regression model enabled the materials parameter, that is, 10 Dq, to be automatically estimated from the spectra. With regard to research progress in similarity measures, this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data. Robustness (evolution) ; Similarity (geometry) ; Similarity measure ; Estimation theory ; Computer science ; Statistics ; Biological system ; Artificial intelligence ; Mathematics ; Algorithm ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques