jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emma Vargo | Jakob C. Dahl | Katherine Evans | Tasneem Khan | A. Paul Alivisatos | Ting Xu
Source: Advanced Materials (article, 2022-06-15)
Field: Materials Science — Materials Chemistry

Using Machine Learning to Predict and Understand Complex Self‐Assembly Behaviors of a Multicomponent Nanocomposite Blends of nanoparticles, polymers, and small molecules can self-assemble into optical, magnetic, and electronic devices with structure-dependent properties. However, the relationship between a multicomponent nanocomposite's formulation and its assembled structure is complex and cannot be predicted by theory. The blends can be strongly influenced by processing conditions, which can introduce non-equilibrium states. Currently, nanocomposite devices are designed through cycles of experimental trial and error. Machine learning (ML) methods are a compelling alternative because they can use existing datasets to map high-dimensional spaces. These methods do not rely on known relationships between parameters, so they are suited to complex systems without a solid theoretical foundation. Here, a dataset of 595 microscopy images of nanocomposite thin films is used to train a series of ML models. Correlations between the input and output parameters are examined, providing new insights into the system. Finally, the most successful ML model is used to predict the structures of new nanocomposite compositions. The results confirm that ML techniques can be used to improve the efficiency of nanocomposite device design. More broadly, the current study suggests some of the advantages and challenges associated with applying ML to complex systems. Nanocomposite ; Materials science ; Polymer ; Nanotechnology ; Complex system ; Polymer nanocomposite ; Computer science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Filippos Sofos | Avraam Charakopoulos | Konstantinos Papastamatiou | Theodoros E. Karakasidis
Source: Physics of Fluids (article, 2022-05-21)
Field: Materials Science — Materials Chemistry

A combined clustering/symbolic regression framework for fluid property prediction Symbolic regression techniques are constantly gaining ground in materials informatics as the machine learning counterpart capable of providing analytical equations exclusively derived from data. When the feature space is unknown, unsupervised learning is incorporated to discover and explore hidden connections between data points and may suggest a regional solution, specific for a group of data. In this work, we develop a Lennard–Jones fluid descriptor based on density and temperature values and investigate the similarity between data corresponding to diffusion coefficients. Descriptions are linked with the aid of clustering algorithms, which lead to fluid groups with similar behavior, bound to physical laws. Keeping in mind that the fluid data space goes over the gas, liquid, and supercritical states, we compare clustering results to this categorization and found that the proposed methods can detect the gas and liquid states, while distinct supercritical region characteristics are discovered, where fluid density and temperature affect the diffusion coefficient in a more complex way. The incorporation of symbolic regression algorithms on each cluster provides an in-depth investigation on fluid behavior, and regional expressions are proposed. Cluster analysis ; Physics ; Diffusion ; Supercritical fluid ; Cluster (spacecraft) ; Categorization ; Similarity (geometry) ; Statistical physics ; Artificial intelligence ; Data mining ; Computer science ; Thermodynamics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Dipendra Jha | Vishu Gupta | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Scientific Reports (article, 2022-07-13)
Field: Materials Science — Materials Chemistry

Moving closer to experimental level materials property prediction using AI While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting "formation energy of a material given its structure and composition". On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of [Formula: see text] eV/atom) for the first time. Computation ; Computer science ; Atom (system on chip) ; Task (project management) ; Set (abstract data type) ; Density functional theory ; Test set ; Property (philosophy) ; Algorithm ; Energy (signal processing) ; Statistical physics ; Computational chemistry ; Artificial intelligence ; Mathematics ; Physics ; Chemistry ; Statistics ; Parallel computing Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Nur Maisarah Ruslan | Muhammad Syarifuddin Yahya | Md. Nurul Islam Siddique | Ashish Yengantiwar | M. Ismail | Md. Rabiul Awal | Mohd Zaki Mohd Yusoff | Muhammad Firdaus Asyraf Abdul Halim Yap | N.S. Mustafa
Source: Crystals (article, 2022-09-28)
Field: Materials Science — Materials Chemistry

Review on Magnesium Hydride and Sodium Borohydride Hydrolysis for Hydrogen Production Metal hydrides such as MgH2 and NaBH4 are among the materials for with the highest potential solid-state hydrogen storage. However, unlike gas and liquid storage, a dehydrogenation process has to be done prior to hydrogen utilization. In this context, the hydrolysis method is one of the possible methods to extract or generate hydrogen from the materials. However, problems like the MgH2 passivation layer, high cost and sluggish self-hydrolysis of NaBH4 are the known limiting factors for this process, but they can be overcome with the help of catalysts. In this works, selected studies have been reviewed on the performance of catalysts like chloride, oxide, fluoride, platinum, ruthenium, cobalt and nickel-based on the MgH2 and NaBH4 system. These studies show a significant enhancement in the amount of hydrogen released as compared to the hydrolysis of the pure MgH2 and NaBH4. Therefore, the addition of catalysts is proven as one of the options in improving hydrogen generation via the hydrolysis of MgH2 and NaBH4. Dehydrogenation ; Hydrogen storage ; Sodium borohydride ; Catalysis ; Chemistry ; Inorganic chemistry ; Hydrolysis ; Hydrogen ; Ammonia borane ; Hydrogen production ; Magnesium hydride ; Context (archaeology) ; Borohydride ; Oxide ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Qinghua Luo | Jiao Peng | Xiaoyu Chen | Hui Zhang | Xia Deng | Shiwei Jin | Hai Zhu
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-07-13)
Field: Materials Science — Surfaces, Coatings and Films

Recent Advances in Multifunctional Mechanical–Chemical Superhydrophobic Materials In recent years, biology-inspired superhydrophobic technology has attracted extensive attention and has been widely used in self-cleaning, anti-icing, oil-water separation, and other fields. However, the poor durability restricts its application in practice; thus, it is urgent to systematically summarize it so that scientists can guide the future development of this field. Here, in this review, we first elucidated five kinds of typical superhydrophobic models, namely, Young's equation, Wenzel, Cassie-Baxter, Wenzel-Cassie, "Lotus," and "Gecko" models. Then, we summarized the improvement in mechanical stability and chemical stability of superhydrophobic surface. Later, the durability test methods such as mechanical test methods and chemical test methods are discussed. Afterwards, we displayed the applications of multifunctional mechanical-chemical superhydrophobic materials, namely, anti-fogging, self-cleaning, oil-water separation, antibacterial, membrane distillation, battery, and anti-icing. Finally, the outlook and challenge of mechanical-chemical superhydrophobic materials are highlighted. Durability ; Nanotechnology ; Lotus effect ; Materials science ; Composite material ; Chemistry Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Icing and De-icing Technologies
Authors: Mônica Villa Nova | Tzu Ping Lin | Saeed Shanehsazzadeh | Kanthashree Jain | Samuel Cheng Yong Ng | Richard Wacker | Karim Chichakly | Matthias G. Wacker
Source: Frontiers in Digital Health (review, 2022-02-18)
Field: Materials Science — Materials Chemistry

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, "big data" approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations. Nanomedicine ; Facilitator ; Variety (cybernetics) ; Big data ; Computer science ; Artificial intelligence ; Data science ; Management science ; Engineering ; Psychology Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Shazia Perveen | Raziya Nadeem | Farhat Nosheen | Muhammad Imran Asjad | Jan Awrejcewicz | Tauseef Anwar
Source: Nanomaterials (article, 2022-08-17)
Field: Chemistry — Analytical Chemistry

Biochar-Mediated Zirconium Ferrite Nanocomposites for Tartrazine Dye Removal from Textile Wastewater To meet the current challenges concerning the removal of dyes from wastewater, an environmentally friendly and efficient treatment technology is urgently needed. The recalcitrant, noxious, carcinogenic and mutagenic compound dyes are a threat to ecology and its removal from textile wastewater is challenge in the current world. Herein, biochar-mediated zirconium ferrite nanocomposites (BC-ZrFe<sub>2</sub>O<sub>5</sub> NCs) were fabricated with wheat straw-derived biochar and applied for the adsorptive elimination of Tartrazine dye from textile wastewater. The optical and structural properties of synthesized BC-ZrFe<sub>2</sub>O<sub>5</sub> NCs were characterized via UV/Vis spectroscopy, Fourier transform Infra-red (FTIR), X-Ray diffraction (XRD), Energy dispersive R-Ray (EDX) and Scanning electron microscopy (SEM). The batch modes experiments were executed to explore sorption capacity of BC-ZrFe<sub>2</sub>O<sub>5</sub> NCs at varying operative conditions, i.e., pH, temperature, contact time, initial dye concentrations and adsorbent dose. BC-ZrFe<sub>2</sub>O<sub>5</sub> NCs exhibited the highest sorption efficiency among all adsorbents (wheat straw biomass (WSBM), wheat straw biochar (WSBC) and BC-ZrFe<sub>2</sub>O<sub>5</sub> NCs), having an adsorption capacity of (mg g<sup>-1</sup>) 53.64 ± 0.23, 79.49 ± 0.21 and 89.22 ± 0.31, respectively, for Tartrazine dye at optimum conditions of environmental variables: pH 2, dose rate 0.05 g, temperature 303 K, time of contact 360 min and concentration 100 mg L<sup>-1</sup>. For the optimization of process variables, response surface methodology (RSM) was employed. In order to study the kinetics and the mechanism of the adsorption process, kinetic and equilibrium mathematical models were used, and results revealed 2nd order kinetics and a multilayer chemisorption mechanism due to complexation of hydroxyl, Fe and Zr with dyes functional groups. The nanocomposites were also recovered in five cycles without significant loss (89 to 63%) in adsorption efficacy. This research work provides insight into the fabrication of nanoadsorbents for the efficient adsorption of Tartrazine dye, which can also be employed for practical engineering applications on an industrial scale as efficient and cost effective materials. Biochar ; Tartrazine ; Sorption ; Adsorption ; Wastewater ; Nanocomposite ; Fourier transform infrared spectroscopy ; Malachite green ; Materials science ; Nuclear chemistry ; Chemical engineering ; Langmuir adsorption model ; Response surface methodology ; Leaching (pedology) ; Chemistry ; Waste management ; Chromatography ; Organic chemistry ; Pyrolysis ; Composite material Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Rossella Santonocito | Manuelamaria Intravaia | Ivana Maria Caruso | Andrea Pappalardo | Giuseppe Trusso Sfrazzetto | Nunzio Tuccitto
Source: Nanoscale Advances (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Fluorescence sensing by carbon nanoparticles Sensing is one of the most important fields in which chemists, engineers and other scientists are involved to realize sensoristic devices that can detect different analytes, both chemicals and biologicals. In this context, fluorescence sensing paves the way for the realization of smart sensoristic devices due to the possibility to detect the target analyte <i>via</i> a change in colour or emission. Recently (since 2006), carbon nanoparticles, which are a "new class" of nanostructures based on carbon atoms, have been widely used in sensing applications due to their intriguing optical properties. The scientific literature on this topic started from 2006 and a progressive increase in the corresponding number of publications has been observed. This review summarises the application of carbon nanoparticles in the sensing field, focusing on chemical and ion sensing. Carbon Nanoparticles ; Fluorescence ; Nanoparticle ; Carbon fibers ; Nanotechnology ; Environmental science ; Materials science ; Environmental chemistry ; Remote sensing ; Chemistry ; Geology ; Composite material ; Optics ; Physics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Lucie Nováková | Lenka Novotná | Michaela Procházková | A Alipour | A Khodayari | A Jafari | Z Adams | T Glck | M Badamvaanchig | M Islam | M Kakinaka | L Barbaglia | Ch | I Croux | Wilms | Y Bilan | B Gavurova | G Stanislaw | A Tkacova | G Brenes | H Antonio | T Bollerslev | B Hood | J Huss | Lasse Pedersen | E Cihelkov | H Nguyen | M Fabu | G Daw | S Ding | Y Zhang | M Packova | M Misankova | L Smrcka | M Rigelsky | V Ivankova | J Ge | T Li | H Geman | W Smith | B Liu | Jim Griffin | G Mitrodima | J Guzmn | E Silva | J Horak | V Machova | J Hork | T Krulick | T Hothorn | L Mst | P Bhlmann | M Hudkov | H Urbancov | L Vnoukov | Alan Hutson | D Yu | H | A Kasych | Z Rowland | Y Yakovenko | M Kelemen | J Jevk | V Polishchuk | B Gavurov | R Andoga | S Szabo | W Yang | J Christodoulakis | M Gera | J Kozuba | P Kaavsk | M Antoko | J Kim Min | S Kim | Y Jo | T Koplyay | H Hurta | A Jazouli | M Malouin | T Krulicky | A Krzymowski | N Kumar | J Kumar | K Kurumatani | A Lewandowska | M Stopa | E Inglot-Brzk | F Lutzenberger | B Gleich | H Mayer | Ch Stepanek | A Rathgeber | O Masood
Source: Acta Montanistica Slovaca (article, 2022-05-31)
Field: Chemistry — Analytical Chemistry

Predicted Future Development of Imperfect Complementary Goods – Copper and Zinc Until 2030 The objective of the paper was to evaluate the mutual relationship of copper and zinc prices between 2011 and 2021 and to predict their future prices until the year 2030. For this purpose, the following methods were used: regression of neural structures in TIBCO's Statistica software, version 13.0, time-series smoothing by means of multilayer perceptron network, graphical representation, Pearson correlation coefficient, and logical judgment. According to the prediction, the copper price will decrease slightly compared to the preceding years. In 2026, it is expected to stabilize at USD 610/t until the year 2030. Zinc price is expected to increase slightly until the end of the year 2030 when the resulting predicted price is USD 410/t. Pearson correlation coefficient of copper and zinc achieves the approximate value of 0.65. The results thus confirm the fact that these commodities are not perfect complements. First, the mutual relationship of the two commodities indicates that the price of zinc is pushed up mainly by the copper price. On the contrary, the copper price is pushed down by the price of zinc. There is a price convergence between the two commodities. The future development of copper and zinc prices is not subject to unpredictable events, such as a political situation or the aforementioned COVID-19 pandemic. Such a long-term prediction might thus not provide an objective result. Imperfect ; Zinc ; Copper ; Natural resource economics ; Business ; Economics ; Materials science ; Metallurgy Heavy Metals in Plants
Authors: Patricia Skowronek | Marvin Thielert | Eugenia Voytik | Maria C. Tanzer | Fynn M. Hansen | Sander Willems | Özge Karayel | Andreas‐David Brunner | Florian Meier | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-05-31)
Field: Chemistry — Spectroscopy

Rapid and in-depth coverage of the (phospho-)proteome with deep libraries and optimal window design for dia-PASEF ABSTRACT Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry (MS)-based proteomics, because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation – serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility separated (TIMS) quadrupole time-of-flight (TOF) mass spectrometer. This requires alignment of the ion mobility separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared to DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and ion mobility plane. This A utomatic Isolation D esign procedure is implemented in the freely available py_diAID package. In combination with in-depth project-specific proteomics libraries and the Evosep LC system, we reproducibly identified over 7,700 proteins in a human cancer cell line in 44 minutes with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 minutes LC gradients), we consistently quantified more than 6,000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor (EGF) in triplicate 21 minutes runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies. Proteome ; Proteomics ; Mass spectrometry ; Fragmentation (computing) ; Chemistry ; Phosphoproteomics ; Quadrupole time of flight ; Computer science ; Tandem mass spectrometry ; Chromatography ; Biochemistry ; Phosphorylation ; Protein phosphorylation Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Alessandro Leone | Antonia Tamborrino | Sonia Esposto | Antonio Berardi | Maurizio Servili
Source: Foods (article, 2022-09-08)
Field: Chemistry — Analytical Chemistry

Investigation on the Effects of a Pulsed Electric Field (PEF) Continuous System Implemented in an Industrial Olive Oil Plant The aim of this study was to investigate how the treatment of olive paste of the <i>Picholine</i> variety with pulsed electric fields (PEF) under real operating conditions in a large-scale olive oil extraction plant affects the extractability, chemical composition and sensory profile of the oils. The application of pulsed electric fields (PEF) as a non-thermal food processing technology is interesting for many food extraction processes. The results of this study show that pulsed electric fields can be used as a pretreatment before oil separation to increase the extractability of the process and improve the content of functional components. The application of pulsed electric field (PEF) treatment (2.4 kV/cm, 4 kJ/kg, 6 µs pulse width) to olive paste through a continuous system significantly increased the extractability and total concentration of phenols (especially oleuropein derivatives) compared to the control. In addition, the volatile compounds, α-tocopherol, the fatty acid profile and the main legal quality parameters of extra virgin olive oil (EVOO), including free acidity, peroxide values, extinction indices and sensory analysis, were evaluated. The pulsed electric fields (PEF) treatment did not modify these EVOO quality parameters, neither the α-tocopherol content nor the volatile profile. The sensory properties of EVOO were not affected as well as the PEF treatment showed a similar intensity of fruity and pungent attributes without any off-flavor according to the European Union legal standards. An increase in the bitter taste attribute was observed in the PEF oils. Consequently, this study demonstrates that pulsed electric fields (PEF) processing could be implemented in olive oil processing as pretreatment for improving the efficiency of the process. Electric field ; Olive oil ; Process engineering ; Biochemical engineering ; Materials science ; Biotechnology ; Environmental science ; Chemistry ; Engineering ; Biology ; Food science ; Physics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies