jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chao-Wen Chang | Isaiah Borne | Robin Lawler | Zhenzi Yu | Seung Soon Jang | Ryan P. Lively | David S. Sholl
Source: Journal of the American Chemical Society (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

Accelerating Solvent Selection for Type II Porous Liquids Type II porous liquids, comprising intrinsically porous molecules dissolved in a liquid solvent, potentially combine the adsorption properties of porous adsorbents with the handling advantages of liquids. Previously, discovery of appropriate solvents to make porous liquids had been limited to direct experimental tests. We demonstrate an efficient screening approach for this task that uses COSMO-RS calculations, predictions of solvent p<i>K</i><sub>a</sub> values from a machine-learning model, and several other features and apply this approach to select solvents from a library of more than 11,000 compounds. This method is shown to give qualitative agreement with experimental observations for two molecular cages, CC13 and TG-TFB-CHEDA, identifying solvents with higher solubility for these molecules than had previously been known. Ultimately, the algorithm streamlines the downselection of suitable solvents for porous organic cages to enable more rapid discovery of Type II porous liquids. Chemistry ; Solvent ; Porosity ; Adsorption ; Porous medium ; Solubility ; Molecule ; Organic chemistry ; Chemical engineering Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes ; Radioactive element chemistry and processing
Authors: Guillaume Deffrennes | Kei Terayama | Taichi Abe | Ryo Tamura
Source: Materials & Design (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

A machine learning–based classification approach for phase diagram prediction Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus one of the central tasks in materials science. When exploring new materials for which the phase diagram is unknown, experimentalists often try to determine the key experiments that should be performed by referencing known phase diagrams of similar systems. To enhance this practical strategy, we attempted to estimate unknown phase diagrams based on known phase diagrams using a machine learning–based classification approach. As a proof of concept, we focused on predicting the number of coexisting phases across the 800 K isothermal section of each of the 10 ternaries of the Al-Cu-Mg-Si-Zn system from the other 9 sections. To increase the prediction accuracy, we introduced new descriptors generated from the thermodynamic properties of the elements and CALPHAD extrapolations from lower-order systems. Using the random forest method, the presence of single-, two-, and three-phase domains was predicted with an average accuracy of 84% across all 10 considered sections with a standard deviation of 11%. The proposed approach represents a promising tool for assisting the investigator in developing new materials and determining phase equilibria efficiently. CALPHAD ; Phase diagram ; Isothermal process ; Phase (matter) ; Diagram ; Computer science ; Materials science ; Key (lock) ; Section (typography) ; Machine learning ; Artificial intelligence ; Algorithm ; Thermodynamics ; Physics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; High Temperature Alloys and Creep
Authors: Emmanuel Anuoluwa Bamidele | Ahmed Olanrewaju Ijaola | Michael Oluwatosin Bodunrin | Oluwaniyi Ajiteru | Afure Martha Oyibo | Mamookho Elizabeth Makhatha | Eylem Asmatulu
Source: Advanced Engineering Informatics (article, 2022-03-21)
Field: Materials Science — Materials Chemistry

Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances Materials informatics ; Computer science ; Nanomaterials ; Machine learning ; Artificial intelligence ; Informatics ; Scope (computer science) ; Curse of dimensionality ; Nanotechnology ; Engineering ; Materials science ; Health informatics ; Engineering informatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jordi‐Roger Riba | Rosa Cantero | Pol Riba-Mosoll | Rita Puig
Source: Polymers (article, 2022-06-17)
Field: Chemistry — Analytical Chemistry

Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach The textile industry is generating great environmental concerns due to the exponential growth of textile products' consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produced, used, and discarded), thus putting pressure on resources and creating negative environmental impacts. A new textile economy based on the principles of circular economy is needed for a more sustainable textile industry. To help meet this challenge, an efficient collection, classification, and recycling system needs to be implemented at the end-of-life stage of textile products, so as to obtain high-quality recycled materials able to be reused in high-value products. This paper contributes to the classification of post-consumer textile waste by proposing an automatic classification method able to be trained to separate higher-quality textile fiber flows. Our proposal is the use of near-infrared (NIR) spectroscopy combined with a mathematical treatment of the spectra by convolutional neural networks (CNNs) to classify and separate 100% pure samples and binary mixtures of the most common textile fibers. CNN is applied for the first time to the classification of textile samples. A total of 370 textile samples were studied-50% used for calibration and 50% for prediction purposes. The results obtained are very promising (100% correct classification for pure fibers and 90-100% for binary mixtures), showing that the proposed methodology is very powerful, able to be trained for the specific separation of flows, and compatible with the automation of the system at an industrial scale. Textile ; Textile industry ; Process engineering ; Computer science ; Convolutional neural network ; Quality (philosophy) ; Binary classification ; Sustainability ; Raw material ; Fiber ; Environmental science ; Biochemical engineering ; Artificial intelligence ; Pattern recognition (psychology) ; Pulp and paper industry ; Manufacturing engineering ; Materials science ; Engineering ; Support vector machine ; Chemistry ; Composite material Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Francesca Venturini | Michela Sperti | Umberto Michelucci | Arnaud Gucciardi | Vanessa M. Martos | Marco A. Deriu
Source: Journal of Food Engineering (article, 2022-07-01)
Field: Chemistry — Analytical Chemistry

Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil One of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires samples to be sent to approved laboratories, making the quality control an expensive, time-consuming process, that cannot be performed regularly and cannot guarantee the quality of oil up to the point it reaches the consumer. This work presents a new approach that is fast and based on low-cost instrumentation, and which can be easily performed in the field. The proposed method is based on fluorescence spectroscopy and one-dimensional convolutional neural networks and allows to predict five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270 and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast measurement from a low-cost portable fluorescence sensor. The results indicate that the proposed approach gives exceptional results for quality determination through the extraction of the relevant physicochemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality. Process engineering ; Olive oil ; Convolutional neural network ; Computer science ; Quality (philosophy) ; Fluorescence spectroscopy ; Extraction (chemistry) ; Biological system ; Fluorescence ; Environmental science ; Artificial neural network ; Process (computing) ; Biochemical engineering ; Pulp and paper industry ; Artificial intelligence ; Chemistry ; Chromatography ; Engineering ; Physics ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Zhongwei Chen | Yong Guo | Yanpeng Chu | Tingting Chen | Qingwu Zhang | Changxin Li | Juncheng Jiang | Tao Chen | Yuan Yu | Lian X. Liu
Source: Composites Part B Engineering (article, 2022-05-30)
Field: Materials Science — Polymers and Plastics

Solvent-free and electron transfer-induced phosphorus and nitrogen-containing heterostructures for multifunctional epoxy resin Limiting oxygen index ; Fire retardant ; Epoxy ; Char ; Nitrogen ; Solvent ; Chemistry ; Chemical engineering ; Carbon fibers ; Phosphorus ; Thermal stability ; Materials science ; Polymer chemistry ; Pyrolysis ; Organic chemistry ; Composite material ; Composite number Flame retardant materials and properties ; Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Viktor Zaverkin | David Holzmüller | Robin Schuldt | Johannes Kästner
Source: The Journal of Chemical Physics (article, 2022-02-24)
Field: Materials Science — Materials Chemistry

Predicting properties of periodic systems from cluster data: A case study of liquid water The accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical methods are readily available for cluster data but effectively out of scope for periodic structures. We show that local, atom-centered descriptors for machine-learned potentials enable the prediction of bulk properties from cluster model training data, agreeing reasonably well with predictions from bulk training data. We demonstrate such transferability by studying structural and dynamical properties of bulk liquid water with density functional theory and have found an excellent agreement with experimental and theoretical counterparts. Transferability ; Cluster (spacecraft) ; Scope (computer science) ; Liquid water ; Computer science ; Experimental data ; Coupled cluster ; Function (biology) ; Statistical physics ; Wave function ; Atom (system on chip) ; Data mining ; Biological system ; Machine learning ; Physics ; Mathematics ; Molecule ; Thermodynamics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Kasper Tolborg | Johan Klarbring | Alex M. Ganose | Aron Walsh
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Free energy predictions for crystal stability and synthesisability Prediction of crystal stability and synthesisability is crucial for accelerated materials design. We discuss modern free energy methods for building more accurate models and data-driven approaches suitable for screening large chemical spaces. Stability (learning theory) ; Energy (signal processing) ; Crystal structure prediction ; Crystal (programming language) ; Chemical stability ; Computer science ; Materials science ; Statistical physics ; Crystal structure ; Physics ; Thermodynamics ; Chemistry ; Crystallography ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Maria Sattar | Woon‐Hong Yeo
Source: Materials (review, 2022-06-18)
Field: Materials Science — Materials Chemistry

Recent Advances in Materials for Wearable Thermoelectric Generators and Biosensing Devices Recently, self-powered health monitoring systems using a wearable thermoelectric generator (WTEG) have been rapidly developed since no battery is needed for continuous signal monitoring, and there is no need to worry about battery leakage. However, the existing materials and devices have limitations in rigid form factors and small-scale manufacturing. Moreover, the conventional bulky WTEG is not compatible with soft and deformable tissues, including human skins or internal organs. These limitations restrict the WTEG from stabilizing the thermoelectric gradient that is necessary to harvest the maximum body heat and generate valuable electrical energy. This paper summarizes recent advances in soft, flexible materials and device designs to overcome the existing challenges. Specifically, we discuss various organic and inorganic thermoelectric materials with their properties for manufacturing flexible devices. In addition, this review discusses energy budgets required for effective integration of WTEGs with wearable biomedical systems, which is the main contribution of this article compared to previous articles. Lastly, the key challenges of the existing WTEGs are discussed, followed by describing future perspectives for self-powered health monitoring systems. Wearable computer ; Thermoelectric effect ; Thermoelectric generator ; Energy harvesting ; Battery (electricity) ; Computer science ; Wearable technology ; Thermoelectric materials ; Nanotechnology ; Systems engineering ; Materials science ; Energy (signal processing) ; Embedded system ; Engineering ; Power (physics) Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Thermal Radiation and Cooling Technologies
Authors: Xixia Zhang | Jaroslav Kaštyl | Mariano Casas-Luna | Lubomír Havlíček | Marek Vondra | Vladimír Brummer | Kateřina Sukačová | Vítězslav Máša | Sin Yong Teng | Petr Neugebauer
Source: Journal of environmental chemical engineering (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

Microalgae-derived nanoporous biochar for ammonia removal in sustainable wastewater treatment Ammonia is a critical pollutant in biological wastewater and is not easily separated mechanically due to similar physical properties (e.g., molecular weight, polarity, etc.) to water. Currently, an agent for ammonia removal from wastewater using zeolites must be mined, which is not environmental-friendly. The development of a sustainable and biological absorbent for ammonia-containing wastewater is therefore desirable. Since microalgae can grow rapidly by feeding on waste nutrients in wastewater and up-taking carbon dioxide flue gas, it is of high interest to be used as a bio-absorbent for secondary wastewater treatment. The problem statement of this work is to explore and understand the use of microalgae as a biological source for effective ammonia absorbent. In this work, nanoporous microalgae biochar (NP-MBC) is proposed to be synthesized by using formaldehyde-stabilized hydrothermal and KOH-activated high-temperature pyrolysis. The novelty of this work is that the mechanism for NP-MBC ammonia removal is being uncovered via spectroscopic and sorption analysis. Formaldehydestabilization maintains the morphology of the microalgae particles for higher quality pore formation during pyrolysis. Pyrolysis temperatures up to 700 C improve pore structure and surface chemistry of the NP-MBC, leading to a specific surface area of 1137 m 2 g -1 and increased activation of COO -groups for ammonia adsorption. Further pyrolysis at 800 C damages the textual characteristics of the synthesized NP-MBC, causing pores agglomeration and lower ammonia adsorption. The best ammonia adsorption performance was obtained on NP-MBC prepared by pyrolysis at 700 C with maximum ammonia removal of 72 % (within 120 min) and adsorption capacity over 69 mgg -1 . Nanoporous structure by stabilized hydrothermal treatment and hightemperature pyrolysis are interesting as a paradigm to convert particulate biomass feedstock to functional material with tunable properties. Biochar ; Wastewater ; Pyrolysis ; Adsorption ; Chemistry ; Ammonia ; Chemical engineering ; Sorption ; Nanoporous ; Pulp and paper industry ; Environmental chemistry ; Waste management ; Organic chemistry ; Environmental engineering ; Environmental science Catalytic Processes in Materials Science ; Adsorption and biosorption for pollutant removal ; Supercapacitor Materials and Fabrication
Authors: Kanchan Samadhiya | Rimjhim Sangtani | R. Nogueira | Kiran Bala
Source: Frontiers in Microbiology (review, 2022-01-04)
Field: Materials Science — Biomaterials

Insightful Advancement and Opportunities for Microbial Bioplastic Production Impetuous urbanization and population growth are driving increased demand for plastics to formulate impeccable industrial and biomedical commodities. The everlasting nature and excruciating waste management of petroleum-based plastics have catered to numerous challenges for the environment. However, just implementing various end-of-life management techniques for assimilation and recycling plastics is not a comprehensive remedy; instead, the extensive reliance on finite resources needs to be reduced for sustainable production and plastic product utilization. Microorganisms, such as bacteria and algae, are explored substantially for their bioplastic production repertoire, thus replacing fossil-based plastics sooner or later. Nevertheless, the utilization of pure microbial cultures has led to various operational and economical complications, opening the ventures for the usage of mixed microbial cultures (MMCs) consisting of bacteria and algae for sustainable production of bioplastic. The current review is primarily focuses on elaborating the bioplastic production capabilities of different bacterial and algal strains, followed by discussing the quintessence of MMCs. The present state-of-the-art of bioplastic, different types of bacterial bioplastic, microalgal biocomposites, operational factors influencing the quality and quantity of bioplastic precursors, embracing the potential of bacteria-algae consortia, and the current global status quo of bioplastic production has been summarized extensively. Bioplastic ; Business ; Production (economics) ; Engineering ; Biochemical engineering ; Waste management ; Economics biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Catalysis and Immobilization
Authors: Xin Ma
Source: Molecules (review, 2022-09-30)
Field: Chemistry — Spectroscopy

Recent Advances in Mass Spectrometry-Based Structural Elucidation Techniques Mass spectrometry (MS) has become the central technique that is extensively used for the analysis of molecular structures of unknown compounds in the gas phase. It manipulates the molecules by converting them into ions using various ionization sources. With high-resolution MS, accurate molecular weights (MW) of the intact molecular ions can be measured so that they can be assigned a molecular formula with high confidence. Furthermore, the application of tandem MS has enabled detailed structural characterization by breaking the intact molecular ions and protonated or deprotonated molecules into key fragment ions. This approach is not only used for the structural elucidation of small molecules (MW < 2000 Da), but also crucial biopolymers such as proteins and polypeptides; therefore, MS has been extensively used in multiomics studies for revealing the structures and functions of important biomolecules and their interactions with each other. The high sensitivity of MS has enabled the analysis of low-level analytes in complex matrices. It is also a versatile technique that can be coupled with separation techniques, including chromatography and ion mobility, and many other analytical instruments such as NMR. In this review, we aim to focus on the technical advances of MS-based structural elucidation methods over the past five years, and provide an overview of their applications in complex mixture analysis. We hope this review can be of interest for a wide range of audiences who may not have extensive experience in MS-based techniques. Chemistry ; Mass spectrometry ; Biomolecule ; Tandem mass spectrometry ; Characterization (materials science) ; Protonation ; Molecule ; Ion-mobility spectrometry ; Small molecule ; Ion ; Nanotechnology ; Chromatography ; Materials science ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Teodora Constantinescu | Alin Grig Mihis
Source: International Journal of Molecular Sciences (review, 2022-09-30)
Field: Chemistry — Organic Chemistry

Two Important Anticancer Mechanisms of Natural and Synthetic Chalcones ATP-binding cassette subfamily G and tubulin pharmacological mechanisms decrease the effectiveness of anticancer drugs by modulating drug absorption and by creating tubulin assembly through polymerization. A series of natural and synthetic chalcones have been reported to have very good anticancer activity, with a half-maximal inhibitory concentration lower than 1 µM. By modulation, it is observed in case of the first mechanism that methoxy substituents on the aromatic cycle of acetophenone residue and substitution of phenyl nucleus by a heterocycle and by methoxy or hydroxyl groups have a positive impact. To inhibit tubulin, compounds bind to colchicine binding site. Presence of methoxy groups, amino groups or heterocyclic substituents increase activity. Tubulin ; Chemistry ; Colchicine ; Chalcone ; Acetophenone ; Stereochemistry ; Residue (chemistry) ; Microtubule ; Biochemistry ; Biology Synthesis and biological activity ; Click Chemistry and Applications ; Synthesis and Characterization of Heterocyclic Compounds