jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alex James | S. W. Harding | Thomas J. Robshaw | Neil Bramall | Mark D. Ogden | Robert Dawson
Source: ACS Applied Materials & Interfaces (article, 2019-05-29)
Field: Materials Science — Materials Chemistry

Selective Environmental Remediation of Strontium and Cesium Using Sulfonated Hyper-Cross-Linked Polymers (SHCPs) Sulfonated hyper-cross-linked polymers based on 4,4'-bis(chloromethyl)-1,1'-biphenyl (BCMBP) were synthesized via metal-free (SHCP-1) and conventional Lewis acid-catalyzed (SHCP-2) Friedel-Crafts alkylation routes. The sulfonated polymers possessed BET surface areas in excess of 500 m<sup>2</sup>·g<sup>-1</sup>. SHCP-1 was investigated for its ability to extract Sr and Cs ions from aqueous solutions via the ion-exchange reaction of the sulfonic acid moiety. Equilibrium uptake data could be accurately modeled by the Dubinin-Radushkevich isotherm, with maximum calculated loading values of 95.6 ± 2.8 mg·g<sup>-1</sup> (Sr) and 273 ± 37 mg·g<sup>-1</sup> (Cs). Uptake of both target ions was rapid, with pseudo second-order rate constants calculated as 7.71 ± 1.1 (×10<sup>-2</sup>) for Sr and 0.113 ± 0.014 for Cs. Furthermore, the polymer was found to be highly selective toward the target ions over large excesses of naturally occurring competing metal ions Na, K, Mg, and Ca. We conclude that hyper-cross-linked polymers may offer intrinsic advantages over other adsorbents for the remediation of aqueous Sr and Cs contamination. Strontium ; Materials science ; Environmental remediation ; Caesium ; Polymer ; Human decontamination ; Nanotechnology ; Chemical engineering ; Inorganic chemistry ; Waste management ; Contamination ; Organic chemistry ; Composite material ; Chemistry Covalent Organic Framework Applications ; Chemical Synthesis and Characterization ; Radioactive element chemistry and processing
Authors: Yuma Iwasaki | Ichiro Takeuchi | Valentin Stanev | A. Gilad Kusne | Masahiko Ishida | Akihiro Kirihara | Kazuki Ihara | Ryohto Sawada | Koichi Terashima | Hiroko Someya | Ken‐ichi Uchida | Eiji Saitoh | Shinichi Yorozu
Source: Scientific Reports (article, 2019-02-26)
Field: Materials Science — Materials Chemistry

Machine-learning guided discovery of a new thermoelectric material Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect. In such nascent scientific field, data-driven approaches relying on statistics and machine learning, instead of more traditional modeling methods, can exhibit their full potential. Here, we use machine learning modeling to establish the key physical parameters controlling STE. Guided by the models, we have carried out actual material synthesis which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation of STE devices. Thermoelectric effect ; Scalability ; Computer science ; Thermoelectric materials ; Field (mathematics) ; Identification (biology) ; Data science ; Thermoelectric generator ; Machine learning ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Pavel Korotaev | I.I. Novoselov | Aleksey Yanilkin | Alexander V. Shapeev
Source: Physical review. B./Physical review. B (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Accessing thermal conductivity of complex compounds by machine learning interatomic potentials While lattice thermal conductivity is an important parameter for many technological applications, its calculation is a time-consuming task, especially for compounds with a complex crystal structure. In this paper, we solve this problem using machine learning interatomic potentials. These potentials trained on the density functional theory results and provide an accurate description of lattice dynamics. Additionally, active learning was applied to significantly reduce the number of expensive quantum-mechanical calculations required for training and increases reliability of the potential. The ${\mathrm{CoSb}}_{3}$ skutterudite was considered as an example, and the solution of the Boltzmann transport equation for phonons was compared with the Green-Kubo method. We demonstrated that accurate and reliable potentials can be obtained by performing just a few hundred quantum-mechanical calculations. The potentials reproduce not only the vibrational spectrum, but also the lattice thermal conductivity, as calculated by various methods. Thermal conductivity ; Phonon ; Skutterudite ; Statistical physics ; Lattice (music) ; Quantum ; Interatomic potential ; Molecular dynamics ; Computer science ; Materials science ; Condensed matter physics ; Physics ; Quantum mechanics ; Thermoelectric materials Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Thermoelectric Materials and Devices
Authors: Ian W. Davies
Source: Nature (review, 2019-06-01)
Field: Materials Science — Materials Chemistry

The digitization of organic synthesis Organic chemistry has largely been conducted in an ad hoc manner by academic laboratories that are funded by grants directed towards the investigation of specific goals or hypotheses. Although modern synthetic methods can provide access to molecules of considerable complexity, predicting the outcome of a single chemical reaction remains a major challenge. Improvements in the prediction of 'above-the-arrow' reaction conditions are needed to enable intelligent decision making to select an optimal synthetic sequence that is guided by metrics including efficiency, quality and yield. Methods for the communication and the sharing of data will need to evolve from traditional tools to machine-readable formats and open collaborative frameworks. This will accelerate innovation and require the creation of a chemistry commons with standardized data handling, curation and metrics. Computer science ; Digitization ; Data science ; Quality (philosophy) ; Process (computing) ; Artificial intelligence Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Andreas Prestel | Nanna Wichmann | João M. Martins | Riccardo Marabini | Noah Kassem | Sebastian S. Broendum | Marit Otterlei | Olaf Nielsen | Martin Willemoës | Michael Ploug | Wouter Boomsma | Birthe B. Kragelund
Source: Cellular and Molecular Life Sciences (article, 2019-05-27)
Field: Materials Science — Materials Chemistry

The PCNA interaction motifs revisited: thinking outside the PIP-box Proliferating cell nuclear antigen (PCNA) is a cellular hub in DNA metabolism and a potential drug target. Its binding partners carry a short linear motif (SLiM) known as the PCNA-interacting protein-box (PIP-box), but sequence-divergent motifs have been reported to bind to the same binding pocket. To investigate how PCNA accommodates motif diversity, we assembled a set of 77 experimentally confirmed PCNA-binding proteins and analyzed features underlying their binding affinity. Combining NMR spectroscopy, affinity measurements and computational analyses, we corroborate that most PCNA-binding motifs reside in intrinsically disordered regions, that structure preformation is unrelated to affinity, and that the sequence-patterns that encode binding affinity extend substantially beyond the boundaries of the PIP-box. Our systematic multidisciplinary approach expands current views on PCNA interactions and reveals that the PIP-box affinity can be modulated over four orders of magnitude by positive charges in the flanking regions. Including the flanking regions as part of the motif is expected to have broad implications, particularly for interpretation of disease-causing mutations and drug-design, targeting DNA-replication and -repair. Proliferating cell nuclear antigen ; DNA replication ; Sequence motif ; Computational biology ; DNA ; Biology ; Structural motif ; Genetics ; Plasma protein binding ; Drug discovery ; Cell biology ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; DNA Repair Mechanisms
Authors: Kurt A. Terrani | Brian Jolly | Michael Trammell
Source: Journal of the American Ceramic Society (article, 2019-10-30)
Field: Materials Science — Ceramics and Composites

3D printing of high‐purity silicon carbide Abstract A method for advanced manufacturing of silicon carbide offering complete freedom in geometric complexity in the three‐dimensional space is described. The method combines binder jet printing and chemical vapor infiltration in a process capable of yielding a high‐purity, fully crystalline ceramic—attributes essential for ideal performance in very high‐temperature applications or in the presence of displacement damage. Thermal conductivity and characteristic equibiaxial flexural strength of the resulting monolithic SiC at room temperature are 37 W·(m·K) −1 and 297 MPa, respectively. Silicon carbide ; Materials science ; Ceramic ; Thermal conductivity ; Flexural strength ; Composite material ; Silicon ; Chemical vapor infiltration ; Thermal ; Carbide ; Optoelectronics Advanced ceramic materials synthesis ; Advanced materials and composites ; Semiconductor materials and devices
Authors: Hui Li | Rama M. Shakaroun | Sophie M. Guillaume | Jean‐François Carpentier
Source: Chemistry - A European Journal (review, 2019-10-07)
Field: Materials Science — Biomaterials

Recent Advances in Metal‐Mediated Stereoselective Ring‐Opening Polymerization of Functional Cyclic Esters towards Well‐Defined Poly(hydroxy acid)s: From Stereoselectivity to Sequence‐Control Poly(hydroxy acid)s are a family of biocompatible and (bio)degradable polyesters with various outcomes in different domains of application. To date, poly(hydroxy acid)s are best prepared by ring-opening polymerization (ROP) of the corresponding cyclic esters. Using racemic chiral monomers featuring side-chain groups enables to access, providing a stereoselective catalyst/initiator system is implemented, stereoregular functional polymers, thereby improving their physico-chemical properties, and ultimately, widening their range of uses. Here, we highlight a few important advances in metal-mediated stereoselective ROP of cyclic esters towards the synthesis of (functional) stereoregular poly(hydroxy acid)s that have recently been disclosed, emphasizing on (functional) β- and γ-lactones, diolide and O-carboxyanhydride (OCA) monomers and yttrium-based catalysis. Fine-tuning of the substituents flanked on the catalyst ligand enables reaching poly(hydroxy acid)s with syndiotactic and also isotactic microstructures. The stereocontrol mechanisms at work and their probable origin, relying on steric but also electronic factors imparted in particular by the ligand substituents, are discussed. Taking advantage of such stereoselective ROPs, original copoly(hydroxy acid)s with gradient or alternated patterns then become accessible from the use of mixtures of chemically different, oppositely configured enantiopure monomers. Stereoselectivity ; Enantiopure drug ; Polymerization ; Lactide ; Monomer ; Ring-opening polymerization ; Ligand (biochemistry) ; Polymer chemistry ; Chemistry ; Steric effects ; Tacticity ; Polyester ; Combinatorial chemistry ; Catalysis ; Polymer ; Materials science ; Stereochemistry ; Enantioselective synthesis ; Organic chemistry biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Polyoxometalates: Synthesis and Applications
Authors: Shyue Ping Ong
Source: Computational Materials Science (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Accelerating materials science with high-throughput computations and machine learning Throughput ; Scalability ; Computer science ; Automation ; Computation ; Software ; Efficient energy use ; Computational science ; Density functional theory ; Characterization (materials science) ; Computer engineering ; Machine learning ; Computer architecture ; Artificial intelligence ; Nanotechnology ; Materials science ; Chemistry ; Mechanical engineering ; Engineering ; Database ; Algorithm Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Çiğdem Altıntaş | Gökay Avcı | Hilal Daglar | Ayda Nemati Vesali Azar | İlknur Eruçar | Sadiye Velioğlu | Seda Keskın
Source: Journal of Materials Chemistry A (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

An extensive comparative analysis of two MOF databases: high-throughput screening of computation-ready MOFs for CH<sub>4</sub> and H<sub>2</sub> adsorption An extensive comparative analysis of two computation-ready MOF databases was provided to study adsorption and separation of CH<sub>4</sub> and H<sub>2</sub>. Computation ; Adsorption ; Database ; Throughput ; Computer science ; Computational science ; Chemistry ; Materials science ; Parallel computing ; Physical chemistry ; Algorithm ; Operating system Metal-Organic Frameworks: Synthesis and Applications ; Inorganic Fluorides and Related Compounds ; Industrial Gas Emission Control
Authors: Subhankar Singha | Yong Woong Jun | Sourav Sarkar | Kyo Han Ahn
Source: Accounts of Chemical Research (review, 2019-08-30)
Field: Chemistry — Spectroscopy

An Endeavor in the Reaction-Based Approach to Fluorescent Probes for Biorelevant Analytes: Challenges and Achievements The promising features of fluorescence spectroscopy have inspired a quest for fluorescent probes for analysis and monitoring of molecular interactions in biochemical, medical, and environmental sciences. To overcome the competitive supramolecular interactions in aqueous media encountered with conventional molecular-recognition-based probes, the use of reaction-based probes that involve making or breaking of covalent bonds has emerged as a complementary sensing strategy to realize higher selectivity and sensitivity with larger spectroscopic changes. In spite of the enormous efforts, the development of reaction-based fluorescent probes meets with certain challenges in terms of their practical applications, demanding "intelligent design" of probes with an appropriate fluorophore attached to an efficient reactive moiety at the right place. This Account summarizes the results of our efforts made in the development and fine-tuning of reaction-based fluorescent probes toward those goals, classified by the type of analyte (anions, metal cations, and biomolecules) with notes on the challenges and achievements. The reaction-based approach was demonstrated to be powerful for the selective sensing of anions (cyanide and (amino)carboxylates) for the first time, and later it was extended to develop two-photon probes for bisulfite and fluoride ions. The reaction-based approach also enabled selective sensing of noble metal ions such as silver, gold, and palladium along with toxic (methyl)mercury species and paramagnetic copper ions. Furthermore, microscopic imaging and monitoring of biologically relevant species with reaction-based two-photon probes were explored for hydrogen sulfide, hypochlorous acid, formaldehyde, monoamine oxidase enzyme, and ATP. Analyte ; Fluorescence ; Chemistry ; Combinatorial chemistry ; Biochemical engineering ; Computer science ; Nanotechnology ; Chromatography ; Engineering ; Materials science ; Physics Molecular Sensors and Ion Detection ; Sulfur Compounds in Biology ; Electrochemical sensors and biosensors
Authors: Yunchao Xie | Chen Zhang | Xiangquan Hu | Chi Zhang | Steven P. Kelley | Jerry L. Atwood | Jian Lin
Source: Journal of the American Chemical Society (article, 2019-12-24)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules Herein, we report machine learning algorithms by training data sets from a set of both successful and failed experiments for studying the crystallization propensity of metal-organic nanocapsules (MONCs). Among a variety of studied machine learning algorithms, XGBoost affords the highest prediction accuracy of >90%. The derived chemical feature scores that determine importance of reaction parameters from the XGBoost model assist to identify synthesis parameters for successfully synthesizing new hierarchical structures of MONCs, showing superior performance to a well-trained chemist. This work demonstrates that the machine learning algorithms can assist the chemists to faster search for the optimal reaction parameters from many experimental variables, whose features are usually hidden in the high-dimensional space. Chemistry ; Artificial intelligence ; Machine learning ; Chemical space ; Nanocapsules ; Feature (linguistics) ; Nanotechnology ; Computer science ; Drug discovery ; Nanoparticle Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Christoph Wichmann | Florian Meier | Sebastian Virreira Winter | Andreas‐David Brunner | Jürgen Cox | Matthias Mann
Source: Molecular & Cellular Proteomics (article, 2019-02-12)
Field: Chemistry — Spectroscopy

MaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides Mass spectrometry (MS)-based proteomics is often performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. Although achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be fragmented in each run. In contrast, targeted proteomics aims to reproducibly and sensitively record a restricted number of precursor/fragment combinations in each run, based on prescheduled mass-to-charge and retention time windows. Here we set out to unify these two concepts by a global targeting approach in which an arbitrary number of precursors of interest are detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and real-time recalibration in mass, retention time and intensity dimensions to predict precursor identity. MaxQuant.Live is freely available (www.maxquant.live) and has a graphical user interface to specify many predefined data acquisition strategies. Acquisition speed is as fast as with the vendor software and the power of our approach is demonstrated with the acquisition of breakdown curves for hundreds of precursors of interest. We also uncover precursors that are not even visible in MS1 scans, using elution time prediction based on the auto-adjusted retention time alone. Finally, we successfully recognized and targeted more than 25,000 peptides in single LC-MS runs. Global targeting combines the advantages of two classical approaches in MS-based proteomics, whereas greatly expanding the analytical toolbox. MaxQuant.Live builds on the fast application programming interface of quadrupole Orbitrap mass analyzers to control data acquisition in real-time (freely available at www.maxquant.live). Its graphical user interface enables advanced data acquisition strategies, such as in-depth characterization of peptides of interest. Online recalibration in mass, retention time, and intensity dimensions extends this concept to more than 25,000 peptides per run. Our "global targeting" strategy combines the best of targeted and shotgun approaches. Computational biology ; Chemistry ; Computer science ; Cell biology ; Biology Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Ubiquitin and proteasome pathways
Authors: Moinak Dutta | Koushik Pal | Umesh V. Waghmare | Kanishka Biswas
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Bonding heterogeneity and lone pair induced anharmonicity resulted in ultralow thermal conductivity and promising thermoelectric properties in n-type AgPbBiSe<sub>3</sub> Efficiency in generation and utilization of energy is highly dependent on materials that have the ability to amplify or hinder thermal conduction processes. A comprehensive understanding of the relationship between chemical bonding and structure impacting lattice waves (phonons) is essential to furnish compounds with ultralow lattice thermal conductivity (<i>κ</i> <sub>lat</sub>) for important applications such as thermoelectrics. Here, we demonstrate that the n-type rock-salt AgPbBiSe<sub>3</sub> exhibits an ultra-low <i>κ</i> <sub>lat</sub> of 0.5-0.4 W m<sup>-1</sup> K<sup>-1</sup> in the 290-820 K temperature range. We present detailed analysis to uncover the fundamental origin of such a low <i>κ</i> <sub>lat</sub>. First-principles calculations augmented with low temperature heat capacity measurements and the experimentally determined synchrotron X-ray pair distribution function (PDF) reveal bonding heterogeneity within the lattice and lone pair induced lattice anharmonicity. Both of these factors enhance the phonon-phonon scattering, and are thereby responsible for the suppressed <i>κ</i> <sub>lat</sub>. Further optimization of the thermoelectric properties was performed by aliovalent halide doping, and a thermoelectric figure of merit (<i>zT</i>) of 0.8 at 814 K was achieved for AgPbBiSe<sub>2.97</sub>I<sub>0.03</sub> which is remarkable among n-type Te free thermoelectrics. Anharmonicity ; Thermal conductivity ; Lone pair ; Thermoelectric effect ; Materials science ; Thermoelectric materials ; Condensed matter physics ; Thermal ; Type (biology) ; Chemistry ; Thermodynamics ; Physics ; Composite material ; Molecule ; Organic chemistry Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Chalcogenide Semiconductor Thin Films
Authors: Zhen Jiang | Ayana Bhaskaran | Heather M. Aitken | India C. G. Shackleford | Luke A. Connal
Source: Macromolecular Rapid Communications (review, 2019-04-12)
Field: Materials Science — Polymers and Plastics

Using Synergistic Multiple Dynamic Bonds to Construct Polymers with Engineered Properties Dynamic bonds have achieved significant attention for their ability to impart fascinating properties to polymeric materials, such as high mechanical strength, self-healing, shape memory, 3D printability, and conductivity. Incorporating multiple dynamic bonds into polymer systems affords an attractive and efficient approach to endow multiple functionalities. This mini-review focuses on the use of complementary dynamic interactions to control the properties of soft materials. Owing to the diversity in dynamic chemistries that can be explored, the scope of this article is restricted to polymers and does not include colloids, amphiphiles, liquid crystals, or biological soft matter. Polymer ; Soft matter ; Materials science ; Nanotechnology ; Scope (computer science) ; Soft materials ; Shape-memory polymer ; Construct (python library) ; Amphiphile ; Colloid ; Polymer science ; Computer science ; Composite material ; Copolymer ; Chemistry ; Organic chemistry Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; Synthetic Organic Chemistry Methods