jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Válter Vairinhos | Rui Parreira | Suzana Lampreia | Victor Lobo | Purificación Galindo‐Villardón
Source: International Journal of Prognostics and Health Management (article, 2020-11-20)
Field: Chemistry — Analytical Chemistry

Vibration Analysis Based on HJ-Biplots Vibration Analysis (VA) is now routinely used for condition monitoring and failure diagnosis in Condition Based Maintenance (CBM). In the context of VA, a methodology is proposed, based on biplots, to simultaneously display both vibration frequencies and their measurement points, in support of monitoring and diagnostics tasks.In this research, real observational data obtained measuring mechanical vibrations on four generators aboard a Portuguese Navy Ship in real operating conditions is used. A portable vibration collector was employed, and the measurements were taken at 13 measurement points in each one of four generators, using the same collector settings. Spectrograms resulting from vibration measurements were transformed into biplots and used for decision support according to the proposed methodology. Data analysis showed a robust stability in the macrostructure of biplots when observations resulting from different generators of the same model and at the same assumed conditions was analyzed. This invariance allows the specification of reference conditions, rules to detect changes of operating conditions and the emergence of failures. The proposed methodology, once embedded in dedicated software, will reduce the interpretation error in diagnosis and prognosis associated to variability in personnel training and experience. Consequently, it will increase the safe use of VA in an increasing number of situations. Vibration ; Context (archaeology) ; Biplot ; Condition monitoring ; Computer science ; Reliability engineering ; Software ; Navy ; Engineering ; Acoustics ; Geology Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Statistical Methods and Models
Authors: Kirill Shmilovich | Rachael A. Mansbach | Hythem Sidky | Olivia E. Dunne | Sayak Subhra Panda | John D. Tovar | Andrew L. Ferguson
Source: arXiv (Cornell University) (preprint, 2020-01-27)
Field: Materials Science — Biomaterials

Discovery of Self-Assembling $\pi$-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation Electronically-active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from $\pi$-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence-structure-function relations remain poorly characterized. In this work, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically-active $\pi$-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylene vinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 20$^3$ = 8,000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems. Tripeptide ; Conjugated system ; Oligopeptide ; Peptide ; Stacking ; Molecular dynamics ; Chemistry ; Molecule ; Cyclic peptide ; Combinatorial chemistry ; Nanotechnology ; Biological system ; Materials science ; Computational chemistry ; Polymer ; Biochemistry ; Organic chemistry ; Biology Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Shion Takeno | Yuhki Tsukada | Hitoshi Fukuoka | Toshiyuki Koyama | M. Shiga | Masayuki Karasuyama
Source: Physical Review Materials (article, 2020-08-11)
Field: Materials Science — Materials Chemistry

Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling Information regarding precipitate shapes is critical for estimating material parameters. Hence, we considered estimating a region of material parameter space in which a computational model produces precipitates having shapes similar to those observed in the experimental images. This region, called the lower-error region (LER), reflects intrinsic information of the material contained in the precipitate shapes. However, the computational cost of LER estimation can be high because the accurate computation of the model is required many times to better explore parameters. To overcome this difficulty, we used a Gaussian-process-based multifidelity modeling, in which training data can be sampled from multiple computations with different accuracy levels (fidelity). Lower-fidelity samples may have lower accuracy, but the computational cost is lower than that for higher-fidelity samples. Our proposed sampling procedure iteratively determines the most cost-effective pair of a point and a fidelity level for enhancing the accuracy of LER estimation. We demonstrated the efficiency of our method through estimation of the interface energy and lattice mismatch between $\mathrm{Mg}{\mathrm{Zn}}_{2}$ and $\ensuremath{\alpha}\ensuremath{-}\mathrm{Mg}$ phases in an Mg-based alloy. The results showed that the sampling cost required to obtain accurate LER estimation could be drastically reduced. Computation ; Kriging ; Algorithm ; Gaussian ; Materials science ; Sampling (signal processing) ; Gaussian process ; Lattice (music) ; Fidelity ; Process (computing) ; High fidelity ; Mathematical optimization ; Biological system ; Computer science ; Statistical physics ; Mathematics ; Optics ; Physics ; Machine learning Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Florian Häse | Teresa Tamayo-Mendoza | Carmen Boixo | Jonathan Romero | Loı̈c M. Roch | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-04-09)
Field: Chemistry — Physical and Theoretical Chemistry

Autonomous Titration for Chemistry Classrooms: Preparing Students for Digitized Chemistry Laboratories The digitalization of the economy is one of the drivers of the fourth industrial revolution. This trend is already heavily permeating biology laboratories and rapidly moving into chemistry as well. Notably, automated laboratories enhance process quality and intensification while freeing researchers from repetitive tasks. With these societal changes in place, students need to be prepared for the advanced digitization of chemistry and science by teaching fundamental chemistry concepts in combination with emerging Industry 4.0 technologies, including programming and automation. We describe an undergraduate classroom exercise at the interface of chemistry, computer science and engineering based on the development of an autonomous titration platform. Following an inquiry learning ansatz, the exercise focuses on standard titration experiments which are first executed manually, then automatically and finally in full autonomy by a student-designed robotic platform. We demonstrate that the exercise introduced in this work enables students to learn fundamental concepts in analytical chemistry, naturally integrates basic aspects of programming and automation, and as a consequence promotes and reinforces the detailed understanding of experimental processes and measurements. The exercise is designed in a collaborative active learning framework to encourage complex critical thinking and creative problem solving and thus prepares students for the next-generation chemistry laboratories. Computer science ; Chemistry ; Automation ; Process (computing) ; Autonomy ; Digitization ; Mathematics education ; Nanotechnology ; Multimedia ; Engineering ; Psychology ; Political science ; Mechanical engineering ; Materials science Various Chemistry Research Topics ; Microfluidic and Capillary Electrophoresis Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Zheng Cheng | Dongbo Zhao | Jing Ma | Wei Li | Shuhua Li
Source: ChemRxiv (preprint, 2020-05-18)
Field: Materials Science — Materials Chemistry

An On-the-fly Approach to Construct Generalized Energy-Based Fragmentation Machine Learning Force Fields of Complex Systems The paper describes a modification to the generalized energy-based fragmentation (GEBF) method that uses a machine fitted potential energy surface for the subsytems instead of ab initio calculation, in order to speed up the calculations. An on-the-fly active learning is used to construct vaious kind of subsystems force field automatically. Our method can bpyss over 99% of the QM calculations during the ab inito molecular dynamics. Fragmentation (computing) ; Force field (fiction) ; Construct (python library) ; Computer science ; On the fly ; Artificial intelligence Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Electronic and Structural Properties of Oxides
Authors: Tobias Mahnke
Source: British Journal of Visual Impairment (article, 2020-10-11)
Field: Chemistry — Physical and Theoretical Chemistry

Dealing with Lewis structures in chemistry lessons Chemical formulas are represented with the help of Lewis structures. To illustrate reaction mechanisms, these structures are provided with arrows and the new structural formula is recorded. This procedure presents blind pupils with several hurdles: - How do these structures come about? - How can students spontaneously create tactile formulas in class? - How can the movements shown by arrows be traced? For the representation of atomic symbols, for which the element symbols from the periodic table are normally used, magnets were developed that differ in shape, color, and size. Since only a handful of symbols occur regularly and in large numbers in school, only a few symbols have been permanently assigned, some are variable and can be used appropriately defined for the respective teaching situation. With this symbol set, it is possible to quickly and individually manufacture almost all molecules relevant in school. These magnets allow the independent determination of reaction mechanisms in the further course of the lesson. Due to the mobility of the elements on the magnetic base, electrons and atoms can be moved and thus result in a new bonding situation that is reinterpreted by the students. The advantage of this procedure is that the pupils do not just paint arrows, but consciously think about all processes and can also make mistakes. When analyzing intermediate products, you can determine that your actions were either not compliant or compliant but not effective. This enables individual active learning in the classroom. Set (abstract data type) ; Representation (politics) ; Symbol (formal) ; Class (philosophy) ; Element (criminal law) ; Base (topology) ; Mathematics education ; Computer science ; Mathematics ; Artificial intelligence ; Mathematical analysis Various Chemistry Research Topics ; History and advancements in chemistry
Authors: Wenbin Li | Yong Wang | Yanting Feng | Qing Wang | Xuexia Xu | Guowei Li | Guozhen Dong | Shangqian Jing | Ersong Chen | Xiaoliang Fan | Peng Wang
Source: Scanning (article, 2020-12-24)
Field: Materials Science — Surfaces, Coatings and Films

A Cost-Effective Method for Preparing Robust and Conductive Superhydrophobic Coatings Based on Asphalt The wide application of superhydrophobic materials is mainly hindered by the poor mechanical robustness and complicated preparation method. To overcome these problems, we tried to make a combination of hierarchical and self-similar structure by the means of a simple spraying method. By adding nanofiller (carbon nanotube) and microfiller (graphite powder and expanded graphite), the hierarchical structure was constructed. By further doping the fillers in the commercial asphalt uniformly, the self-similar structure was prepared. Based on the aforementioned work, the as-prepared sample could withstand the sandpaper abrasion for 12.00 m under 4.90 kPa. Moreover, this superhydrophobic coating demonstrated good conductivity, superior self-cleaning property, and excellent corrosion resistance. The integration of conductivity with the superhydrophobicity might open new avenues for ground grid applications. Sandpaper ; Materials science ; Graphite ; Composite material ; Coating ; Abrasion (mechanical) ; Conductivity ; Asphalt ; Carbon nanotube ; Nanotechnology Surface Modification and Superhydrophobicity ; High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation
Authors: Tom Young | Joseph J. Silcock | Alistair J. Sterling | Fernanda Duarte
Source: ChemRxiv (preprint, 2020-08-12)
Field: Chemistry — Inorganic Chemistry

autodE: Automated Calculation of Reaction Energy Profiles – Application to Organic and Organometallic Reactions Calculating reaction profiles to aid in mechanistic elucidation has long been the domain of the expert computational chemist. We introduce autodE, an open-source tool capable of locating transition states and minima and delivering a full reaction energy profile with minimal human effort (https://github.com/duartegroup/autodE). autodE is broadly applicable to study organic and organometallic reaction classes, including addition, substitution, elimination, migratory insertion, oxidative addition and reductive elimination; it accounts for conformational sampling of both minima and TSs, and is compatible with many electronic structure packages. The general applicability of autodE is demonstrated in complex multi-step reactions, including metal-catalyzed cobalt- and rhodium-catalyzed hydroformylation, and an Ireland-Claisen rearrangement. Maxima and minima ; Hydroformylation ; Reductive elimination ; Chemistry ; Catalysis ; Combinatorial chemistry ; Rhodium ; Computational chemistry ; Oxidative addition ; Organometallic chemistry ; Organic chemistry ; Mathematics Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Catalytic C–H Functionalization Methods
Authors: Babita Pruseth | Silvi Banerjee | Amit Ghosh
Source: CORD (article, 2020-12-22)
Field: Chemistry — Inorganic Chemistry

Integration of in silico and in vitro approach to reveal the anticancer efficacy of Virgin Coconut Oil Background: Virgin coconut oil (VCO) has antioxidant properties and is being increasingly used as nutraceuticals and cosmeceuticals. It also has a long history of ethnopharmacological use. Anticancer effect of VCO has been reported in several articles. The main bottleneck of exploring the anticancer efficacy of VCO is the difficulty in identification and validation of target proteins and their regulated pathways. Aim: The work plan was in-silico analysis using Comparative Toxicogenomics Database (CTD) and STRING. CTD curated and integrated data for more than 5700 gene-disease and 2000 chemical-disease relationship. Medium Chain Fatty Acids (MCFAs) from VCO like Lauric acid, Caprylic Acid, Capric Acid, and Myristic acid can target almost 17 cancer-associated proteins. Method: Using in silico and in vitro approach, an attempt was made to identify the target proteins and their pathways regulated by VCO. Result: We analyze curated and inferred VCO-gene expression data and illustrate the impact of VCO exposure on cancer-related gene network and molecular function. In enriched pathway analysis, it has been evident that all of them are the part of different cancer-associated pathways (Neoplasms, Digestive System Neoplasms, Urogenital Neoplasms, Liver Neoplasms). This response may mimic the biological response to VCO. In silico result was tested by in vitro study and VCO kill the Human hepatocellular carcinoma cell lines (hepG2). Conclusion: Based on the findings of this study and several published studies it is proposed that a VCO may have immense potential as a botanical product against cancer. In silico ; Toxicogenomics ; Computational biology ; Biology ; Biochemistry ; Gene ; Gene expression Coconut Research and Applications ; Tannin, Tannase and Anticancer Activities ; Protein Hydrolysis and Bioactive Peptides
Authors: Rebecca A. Reiss | Terry C. Lowe | Johnny A. Sena | Oleg Makhnin | Melanie C. Connick | Patrick E. Illescas | Casey F. Davis
Source: PLoS ONE (article, 2020-09-24)
Field: Materials Science — Materials Chemistry

Bio-activating ultrafine grain titanium: RNA sequencing reveals enhanced mechano-activation of osteoconduction on nanostructured substrates Titanium is essentially absent from biological systems yet reliably integrates into bone. To achieve osseointegration, titanium must activate biological processes without entering cells, defining it as a bio-activating material. Nanostructuring bulk titanium reduces grain size, increases strength, and improves other quantifiable physical properties, including cytocompatibility. The biological processes activated by increasing grain boundary availability were detected with total RNA-sequencing in mouse pre-osteoblasts grown for 72 hours on nanometrically smooth substrates of either coarse grain or nanostructured ultrafine grain titanium. The average grain boundary length under cells on the conventional coarse grain substrates is 273.0 μm, compared to 70,881.5 μm for cells adhered to the nanostructured ultrafine grain substrates; a 260-fold difference. Cells on both substrates exhibit similar expression profiles for genes whose products are critical for mechanosensation and transduction of cues that trigger osteoconduction. Biological process Gene Ontology term enrichment analysis of differentially expressed genes reveals that cell cycle, chromatin modification, telomere maintenance, and RNA metabolism processes are upregulated on ultrafine grain titanium. Processes related to immune response, including apoptosis, are downregulated. Tumor-suppressor genes are upregulated while tumor-promoting genes are downregulated. Upregulation of genes involved in chromatin remodeling and downregulation of genes under the control of the peripheral circadian clock implicate both processes in the transduction of mechanosensory information. Non-coding RNAs may also play a role in the response. Merging transcriptomics with well-established mechanobiology principles generates a unified model to explain the bio-activating properties of titanium. The modulation of processes is accomplished through chromatin remodeling in which the nucleus responds like a rheostat to grain boundary concentration. This convergence of biological and materials science reveals a pathway toward understanding the biotic-abiotic interface and will inform the development of effective bio-activating and bio-inactivating materials. Downregulation and upregulation ; Transcriptome ; Cell biology ; Materials science ; Chromatin ; Gene expression ; Titanium ; Biology ; Gene ; Biochemistry ; Metallurgy Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Magnesium Alloys: Properties and Applications
Authors: М. Yu. Belikov | Mikhail Yu. Ievlev | Sergey V. Fedoseev | О. В. Ершов
Source: Research on Chemical Intermediates (article, 2020-04-24)
Field: Materials Science — Materials Chemistry

Synthesis and fine-tuning of thermal stability of the negative nitrile-rich photochromes of hydroxytricyanopyrrole (HTCP) series Nitrile ; Substituent ; Acetonitrile ; Thermal stability ; Chemistry ; Catalysis ; Photochemistry ; Acceptor ; Solvent ; Combinatorial chemistry ; Polymer chemistry ; Organic chemistry ; Physics Photochromic and Fluorescence Chemistry ; Porphyrin and Phthalocyanine Chemistry ; Photochemistry and Electron Transfer Studies
Authors: Á. J. Pérez‐Jiménez | J. C. Sancho-Garcı́a
Source: Advanced Theory and Simulations (article, 2020-09-03)
Field: Chemistry — Organic Chemistry

Theoretical Insights for Materials Properties of Cyclic Organic Nanorings Abstract The synthesis of new carbon nanoforms with remarkable and fine‐tuned bulk properties still represents a formidable challenge, with cyclic organic nanorings emerging in recent years for the template‐driven design of this kind of systems. The design and engineering of these materials can be first controlled at the molecular scale, to further induce their specific self‐assembly toward tailored properties at the nanoscale. Theoretical studies have lately contributed to the understanding of the underlying physical effects, the development of synthetic strategies, and the rationalization of novel materials properties, employing a variety of methods ranging from accurate calculations of isolated molecules to atomistic molecular dynamics simulations of a large sample of molecules in realistic conditions, which will be reviewed here with a focus on the transition from single‐molecule to supramolecular properties. Nanotechnology ; Supramolecular chemistry ; Materials science ; Nanoscopic scale ; Organic molecules ; Molecule ; Chemical physics ; Chemistry ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Organic Electronics and Photovoltaics ; Graphene research and applications
Authors: Ye Il Choi | Cho Chen | Jin Woo Park | Gil S. Lee
Source: Diamond and Related Materials (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

Spinnable carbon nanotube forest synthesis in nitrogen environment Carbon nanotube ; Materials science ; Chemical vapor deposition ; Nanotechnology ; Inert gas ; Nitrogen ; Inert ; Chemical engineering ; Substrate (aquarium) ; Argon ; Nanostructure ; Carbon fibers ; Catalysis ; Spinning ; Chemistry ; Composite material ; Organic chemistry Carbon Nanotubes in Composites ; Supercapacitor Materials and Fabrication ; Graphene research and applications
Authors: Josef Stráský | Jiří Kozlík | Kristína Bartha∥ | Dalibor Preisler | Tomáš Chráska
Source: MATEC Web of Conferences (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Sintering of Ti-based biomedical alloys with increased oxygen content from elemental powders Revived interest for beta Ti alloys with increased oxygen content is motivated by the prospect of achieving material with low modulus and high strength simultaneously. Fine tuning of amount of oxygen and beta stabilizing elements is critical for achieving good mechanical properties. This study shows that powder metallurgy method of spark plasma sintering is capable of producing Ti-Nb-Zr-O alloys from elemental powders. This simple approach allows for quick sampling and production of several alloys with various chemical composition. Elemental powders were mixed with appropriate amount of titanium dioxide to achieve Ti-29Nb-7Zr-0.7O alloy. Sintering was performed at 1400 - 1500 °C for 15 – 30 minutes. Spark plasma sintering ; Materials science ; Powder metallurgy ; Sintering ; Metallurgy ; Alloy ; Elemental analysis ; Titanium ; Oxygen ; Titanium alloy ; Inorganic chemistry ; Chemistry Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Orthopaedic implants and arthroplasty