jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kelly J. Figueroa-López | Luís Cabedo | José M. Lagarón | Sergio Torres‐Giner
Source: Frontiers in Nutrition (article, 2020-09-03)
Field: Materials Science — Biomaterials

Development of Electrospun Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Monolayers Containing Eugenol and Their Application in Multilayer Antimicrobial Food Packaging In this research, different contents of eugenol in the 2.5-25 wt.% range were first incorporated into ultrathin fibers of poly(3-hydroxybutyrate-<i>co</i>-3-hydroxyvalerate) (PHBV) by electrospinning and then subjected to annealing to obtain antimicrobial monolayers. The most optimal concentration of eugenol in the PHBV monolayer was 15 wt.% since it showed high electrospinnability and thermal stability and also yielded the highest bacterial reduction against <i>Staphylococcus aureus</i> (<i>S. aureus</i>) and <i>Escherichia coli</i> (<i>E. coli</i>). This eugenol-containing monolayer was then selected to be applied as an interlayer between a structural layer made of a cast-extruded poly(3-hydroxybutyrate) (PHB) sheet and a commercial PHBV film as the food contact layer. The whole system was, thereafter, annealed at 160°C for 10 s to develop a novel multilayer active packaging material. The resultant multilayer showed high hydrophobicity, strong adhesion and mechanical resistance, and improved barrier properties against water vapor and limonene vapors. The antimicrobial activity of the multilayer structure was also evaluated in both open and closed systems for up to 15 days, showing significant reductions (R ≥ 1 and < 3) for the two strains of food-borne bacteria. Higher inhibition values were particularly attained against <i>S. aureus</i> due to the higher activity of eugenol against the cell membrane of Gram positive (G+) bacteria. The multilayer also provided the highest antimicrobial activity for the closed system, which better resembles the actual packaging and it was related to the headspace accumulation of the volatile compounds. Hence, the here-developed multilayer fully based on polyhydroxyalkanoates (PHAs) shows a great deal of potential for antimicrobial packaging applications using biodegradable materials to increase both quality and safety of food products. Eugenol ; Antimicrobial ; Food science ; Food packaging ; Electrospinning ; Monolayer ; Business ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Polymer biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Silk-based biomaterials and applications
Authors: Christian Schumacher | José G. Hernández | Carsten Bolm
Source: Angewandte Chemie International Edition (article, 2020-06-09)
Field: Chemistry — Organic Chemistry

Electro‐Mechanochemical Atom Transfer Radical Cyclizations using Piezoelectric BaTiO<sub>3</sub> The formation and regeneration of active Cu<sup>I</sup> species is a fundamental mechanistic step in copper-catalyzed atom transfer radical cyclizations (ATRC). Typically, the presence of the catalytically active Cu<sup>I</sup> species in the reaction mixture is secured by using high Cu<sup>I</sup> catalyst loadings or the addition of complementary reducing agents. In this study it is demonstrated how the piezoelectric properties of barium titanate (BaTiO<sub>3</sub> ) can be harnessed by mechanical ball milling to induce electrical polarization in the strained piezomaterial. This strategy enables the conversion of mechanical energy into electrical energy, leading to the reduction of a Cu<sup>II</sup> precatalyst into the active Cu<sup>I</sup> species in copper-catalyzed mechanochemical solvent-free ATRC reactions. Catalysis ; Barium titanate ; Copper ; Piezoelectricity ; Ball mill ; Mechanochemistry ; Atom-transfer radical-polymerization ; Solvent ; Atom (system on chip) ; Chemistry ; Polarization (electrochemistry) ; Chemical engineering ; Materials science ; Photochemistry ; Polymerization ; Organic chemistry ; Composite material ; Physical chemistry ; Ceramic Radical Photochemical Reactions ; Conducting polymers and applications ; Luminescence and Fluorescent Materials
Authors: Edward Kim | Zach Jensen | Alexander van Grootel | Kevin Huang | Matthew Staib | Sheshera Mysore | Haw-Shiuan Chang | Emma Strubell | Andrew McCallum | Stefanie Jegelka | Elsa Olivetti
Source: Journal of Chemical Information and Modeling (article, 2020-01-07)
Field: Materials Science — Materials Chemistry

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated, unsupervised method for connecting scientific literature to inorganic synthesis insights. Starting from the natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for any inorganic materials of interest. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties and that the model's behavior complements the existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds. Computer science ; Autoencoder ; Key (lock) ; Complement (music) ; Pace ; Artificial intelligence ; Word (group theory) ; Artificial neural network ; Language model ; Natural language processing ; Machine learning ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: Troels Lindahl Christiansen | Susan Cooper | Kirsten M. Ø. Jensen
Source: Nanoscale Advances (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

There's no place like real-space: elucidating size-dependent atomic structure of nanomaterials using pair distribution function analysis The development of new functional materials builds on an understanding of the intricate relationship between material structure and properties, and structural characterization is a crucial part of materials chemistry. However, elucidating the atomic structure of nanomaterials remains a challenge using conventional diffraction techniques due to the lack of long-range atomic order. Over the past decade, Pair Distribution Function (PDF) analysis of X-ray or neutron total scattering data has become a mature and well-established method capable of giving insight into the atomic structure in nanomaterials. Here, we review the use of PDF analysis and modelling in characterization of a range of different nanomaterials that exhibit unique atomic structure compared to the corresponding bulk materials. A brief introduction to PDF analysis and modelling is given, followed by examples of how essential structural information can be extracted from PDFs using both model-free and advanced modelling methods. We put an emphasis on how the intuitive nature of the PDF can be used for understanding important structural motifs, and on the diversity of applications of PDF analysis to nanostructure problems. Nanomaterials ; Pair distribution function ; Characterization (materials science) ; Function (biology) ; Distribution function ; Space (punctuation) ; Nanotechnology ; Distribution (mathematics) ; Materials science ; Computer science ; Physics ; Mathematics ; Quantum mechanics ; Biology ; Mathematical analysis X-ray Diffraction in Crystallography ; Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Christopher J. Heard | Lukáš Grajciar | Filip Uhlı́k | Mariya Shamzhy | Maksym Opanasenko | Jiřı́ Čejka | Petr Nachtigall
Source: Advanced Materials (review, 2020-08-11)
Field: Chemistry — Inorganic Chemistry

Zeolite (In)Stability under Aqueous or Steaming Conditions Zeolites are among the most environmentally friendly materials produced industrially at the Megaton scale. They find numerous commercial applications, particularly in catalysis, adsorption, and separation. Under ambient conditions aluminosilicate zeolites are stable when exposed to water or water vapor. However, at extreme conditions as high temperature, high water vapor pressure or increased acidity/basicity, their crystalline framework can be destroyed. The stability of the zeolite framework under aqueous conditions also depends on the concentration and character of heteroatoms (other than Al) and the topology of the zeolite. The factors critical for zeolite (in)stability in the presence of water under various conditions are reviewed from the experimental as well as computational sides. Nonreactive and reactive interactions of water with zeolites are addressed. The goal of this review is to provide a comparative overview of all-silica zeolites, aluminosilicates and zeolites with other heteroatoms (Ti, Sn, and Ge) when contacted with water. Due attention is also devoted to the situation when partial zeolite hydrolysis is used beneficially, such as the formation of hierarchical zeolites, synthesis of new zeolites or fine-tuning catalytic or adsorption characteristics of zeolites. Materials science ; Zeolite ; Steaming ; Aqueous solution ; Chemical engineering ; Organic chemistry ; Catalysis ; Thermodynamics Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications ; Nanopore and Nanochannel Transport Studies
Authors: Jialuo Li | Shuai Yuan | Jun‐Sheng Qin | Jiandong Pang | Peng Zhang | Yingmu Zhang | Yanyan Huang | Hannah F. Drake | Wenshe Ray Liu | Hong‐Cai Zhou
Source: Angewandte Chemie International Edition (article, 2020-03-16)
Field: Chemistry — Inorganic Chemistry

Stepwise Assembly of Turn‐on Fluorescence Sensors in Multicomponent Metal–Organic Frameworks for in Vitro Cyanide Detection The controlled synthesis of multicomponent metal-organic frameworks (MOFs) allows for the precise placement of multiple cooperative functional groups within a framework, leading to emergent synergistic effects. Herein, we demonstrate that turn-on fluorescence sensors can be assembled by combining a fluorophore and a recognition moiety within a complex cavity of a multicomponent MOF. An anthracene-based fluorescent linker and a hemicyanine-containing CN<sup>-</sup> -responsive linker were sequentially installed into the lattice of PCN-700. The selective binding of CN<sup>-</sup> to hemicyanine inhibited the energy transfer between the two moieties, resulting in a fluorescence turn-on effect. Taking advantage of the high tunability of the MOF platform, the ratio between anthracene and the hemicyanine moiety could be fine-tuned in order to maximize the sensitivity of the overall framework. The optimized MOF-sensor had a CN<sup>-</sup> -detection limit of 0.05 μm, which is much lower than traditional CN<sup>-</sup> fluorescent sensors (about 0.2 μm). Cyanide ; Fluorescence ; In vitro ; Metal-organic framework ; Chemistry ; Nanotechnology ; Materials science ; Inorganic chemistry ; Biochemistry ; Organic chemistry ; Physics ; Optics Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Advanced Nanomaterials in Catalysis
Authors: Andrew R. Tyler | Ronnie Ragbirsingh | Charles J. McMonagle | Paul G. Waddell | Sarah E. Heaps | Jonathan W. Steed | Paul Thaw | Michael J. Hall | Michael R. Probert
Source: Chem (article, 2020-05-07)
Field: Chemistry — Physical and Theoretical Chemistry

Encapsulated Nanodroplet Crystallization of Organic-Soluble Small Molecules Single-crystal X-ray diffraction analysis (SCXRD) constitutes a universal approach for the elucidation of molecular structure and the study of crystalline forms. However, the discovery of viable crystallization conditions remains both experimentally challenging and resource intensive in both time and the quantity of analyte(s). We report a robot-assisted, high-throughput method for the crystallization of organic-soluble small molecules in which we employ only micrograms of analyte per experiment. This allows hundreds of crystallization conditions to be screened in parallel with minimal overall sample requirements. Crystals suitable for SCXRD are grown from nanoliter droplets of a solution of analyte in organic solvent(s), each of which is encapsulated within an inert oil to control the rate of solvent loss. This encapsulated nanodroplet crystallization methodology can also be used to search for new crystal forms, as exemplified through both our discovery of a new (13<sup>th</sup>) polymorph of the olanzapine precursor ROY and SCXRD analysis of the "uncrystallizable" agrochemical dithianon. Crystallization ; Analyte ; Solvent ; Materials science ; Organic molecules ; Molecule ; Inert ; Chemistry ; Nanotechnology ; Organic chemistry ; Chromatography Crystallography and molecular interactions ; Analytical Chemistry and Chromatography ; Molecular spectroscopy and chirality
Authors: Brian A. Rohr | Helge S. Stein | Dan Guevarra | Yu Wang | Joel A. Haber | Muratahan Aykol | Santosh K. Suram | John M. Gregoire
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Benchmarking the acceleration of materials discovery by sequential learning Sequential learning (SL) strategies, <i>i.e.</i> iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any "good" material, discovery of all "good" materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery. Benchmarking ; Acceleration ; Computer science ; Data science ; Artificial intelligence ; Physics ; Business Machine Learning in Materials Science ; Mineral Processing and Grinding ; Machine Learning and Algorithms
Authors: Dylan H. Ross | Jang Ho Cho | Libin Xu
Source: Analytical Chemistry (article, 2020-02-25)
Field: Chemistry — Spectroscopy

Breaking Down Structural Diversity for Comprehensive Prediction of Ion-Neutral Collision Cross Sections Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or drug metabolite identification. Ion mobility-mass spectrometry (IM-MS) provides rapid two-dimensional separation of ions based on their mobility through a neutral buffer gas. The mobility of an ion is related to its collision cross section (CCS) with the buffer gas, a physical property that is determined by the size and shape of the ion. This structural dependency makes CCS a promising characteristic for compound identification, but this utility is limited by the availability of high-quality reference CCS values. CCS prediction using machine learning (ML) has recently shown promise in the field, but accurate and broadly applicable models are still lacking. Here we present a novel ML approach that employs a comprehensive collection of CCS values covering a wide range of chemical space. Using this diverse database, we identified the structural characteristics, represented by molecular quantum numbers (MQNs), that contribute to variance in CCS and assessed the performance of a variety of ML algorithms in predicting CCS. We found that by breaking down the chemical structural diversity using unsupervised clustering based on the MQNs, specific and accurate prediction models for each cluster can be trained, which showed superior performance than a single model trained with all data. Using this approach, we have robustly trained and characterized a CCS prediction model with high accuracy on diverse chemical structures. An all-in-one web interface (https://CCSbase.net) was built for querying the CCS database and accessing the predictive model to support unknown compound identifications. Bottleneck ; Ion-mobility spectrometry ; Chemistry ; Identification (biology) ; Cluster analysis ; Chemical space ; Range (aeronautics) ; Data mining ; Biological system ; Computer science ; Mass spectrometry ; Machine learning ; Drug discovery Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Daniele Ongari | Leopold Talirz | Berend Smit
Source: ACS Central Science (review, 2020-10-02)
Field: Materials Science — Materials Chemistry

Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications. Workflow ; Computer science ; Nanoporous ; Field (mathematics) ; Automation ; Data science ; Nanotechnology ; Distributed computing ; Biochemical engineering ; Materials science ; Engineering ; Mechanical engineering Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Dragana Borjan | Maja Leitgeb | Željko Knez | Maša Knez Hrnčič
Source: Molecules (review, 2020-12-15)
Field: Chemistry — Organic Chemistry

Microbiological and Antioxidant Activity of Phenolic Compounds in Olive Leaf Extract According to many reports, phenolic compounds isolated from olive leaves have very good biological activities, especially antimicrobial. Presently, the resistance of microorganisms to antibiotics is greater than ever. Therefore, there are numerous recent papers about alternative solutions for inhibiting their influence on human health. Olive leaf is studied as an important source of antimicrobials with low cost and used in medicine. Numerous publications on involving green technologies for isolation of active compounds from olive leaves have appeared over the past few decades. The present review reports on current knowledge of the most isolated phenolic compounds from olive leaf extract as well as methods for their isolation and characterization. This paper uses recent research findings with a wide range of study models to describe the antimicrobial potential of phenolic compounds. It also describes the vast range of information about methods for determination of antimicrobial potential focusing on effects on different microbes. Additionally, it serves to highlight the role of olive leaf extract as an antioxidants and presents methods for determination of antioxidant potential. Furthermore, it provides an overview of presence of enzymes. The significance of olive leaves as industrial and agricultural waste is emphasized by means of explaining their availability, therapeutic and nutritional effects, and research conducted on this field. Antimicrobial ; Biotechnology ; Isolation (microbiology) ; Antioxidant ; Traditional medicine ; Biology ; Human health ; Food science ; Biochemistry ; Medicine ; Microbiology Edible Oils Quality and Analysis ; Essential Oils and Antimicrobial Activity ; Phytochemicals and Antioxidant Activities
Authors: Rebecca C. Poulos | Peter G. Hains | Rohan Shah | Natasha Lucas | Dylan Xavier | Srikanth S. Manda | Asim Anees | Jennifer M.S. Koh | Sadia Mahboob | Max Wittman | Steven G. Williams | Erin K. Sykes | Michael Hecker | Michael Dausmann | Merridee A. Wouters | Keith Ashman | Jean Yang | Peter J. Wild | Anna DeFazio | Rosemary L. Balleine | Brett Tully | Ruedi Aebersold | Terence P. Speed | Yansheng Liu | Roger R. Reddel | Phillip J. Robinson | Qing Zhong
Source: Nature Communications (article, 2020-07-30)
Field: Chemistry — Spectroscopy

Strategies to enable large-scale proteomics for reproducible research Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics. Computer science ; Pipeline (software) ; Proteomics ; Scale (ratio) ; Mass spectrometry ; Software deployment ; Computational biology ; Data mining ; Biology ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications