jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Łukasz Kozłowski
Source: Nucleic Acids Research (article, 2021-04-12)
Field: Chemistry — Spectroscopy

IPC 2.0: prediction of isoelectric point and p<i>K</i>a dissociation constants The isoelectric point is the pH at which a particular molecule is electrically neutral due to the equilibrium of positive and negative charges. In proteins and peptides, this depends on the dissociation constant (pKa) of charged groups of seven amino acids and NH+ and COO- groups at polypeptide termini. Information regarding isoelectric point and pKa is extensively used in two-dimensional gel electrophoresis (2D-PAGE), capillary isoelectric focusing (cIEF), crystallisation, and mass spectrometry. Therefore, there is a strong need for the in silico prediction of isoelectric point and pKa values. In this paper, I present Isoelectric Point Calculator 2.0 (IPC 2.0), a web server for the prediction of isoelectric points and pKa values using a mixture of deep learning and support vector regression models. The prediction accuracy (RMSD) of IPC 2.0 for proteins and peptides outperforms previous algorithms: 0.848 versus 0.868 and 0.222 versus 0.405, respectively. Moreover, the IPC 2.0 prediction of pKa using sequence information alone was better than the prediction from structure-based methods (0.576 versus 0.826) and a few folds faster. The IPC 2.0 webserver is freely available at www.ipc2-isoelectric-point.org. Isoelectric point ; Isoelectric focusing ; Dissociation constant ; Biology ; Chromatography ; Molecular mass ; Dissociation (chemistry) ; Chemistry ; Biochemistry ; Enzyme ; Physical chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Hafeez Ur Rahim | Muhammad Qaswar | Misbah Uddin | Cinzia Giannini | M. L. Herrera | Giuseppina Rea
Source: Nanomaterials (review, 2021-08-15)
Field: Materials Science — Materials Chemistry

Nano-Enable Materials Promoting Sustainability and Resilience in Modern Agriculture Intensive conventional agriculture and climate change have induced severe ecological damages and threatened global food security, claiming a reorientation of agricultural management and public policies towards a more sustainable development model. In this context, nanomaterials promise to support this transition by promoting mitigation, enhancing productivity, and reducing contamination. This review gathers recent research innovations on smart nanoformulations and delivery systems improving crop protection and plant nutrition, nanoremediation strategies for contaminated soils, nanosensors for plant health and food quality and safety monitoring, and nanomaterials as smart food-packaging. It also highlights the impact of engineered nanomaterials on soil microbial communities, and potential environmental risks, along with future research directions. Although large-scale production and in-field testing of nano-agrochemicals are still ongoing, the collected information indicates improvements in uptake, use efficiency, targeted delivery of the active ingredients, and reduction of leaching and pollution. Nanoremediation seems to have a low negative impact on microbial communities while promoting biodiversity. Nanosensors enable high-resolution crop monitoring and sustainable management of the resources, while nano-packaging confers catalytic, antimicrobial, and barrier properties, preserving food safety and preventing food waste. Though, the application of nanomaterials to the agri-food sector requires a specific risk assessment supporting proper regulations and public acceptance. Food security ; Sustainability ; Business ; Agriculture ; Food safety ; Environmental planning ; Resilience (materials science) ; Environmental resource management ; Environmental science ; Natural resource economics ; Ecology Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution ; Graphene and Nanomaterials Applications
Authors: Federico Olivieri | Rachele Castaldo | Mariacristina Cocca | Gennaro Gentile | Marino Lavorgna
Source: Nanoscale (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Mesoporous silica nanoparticles as carriers of active agents for smart anticorrosive organic coatings: a critical review Mesoporous silica nanoparticles (MSN) have attracted increasing interest for their applicability as smart nanocarriers of corrosion inhibitors, due to their porous structure, resistance to main corrosive environments and good compatibility with polymer coatings. In this review, the main synthetic routes to obtain MSN with tailored textural properties, the design of different loading and stimuli-induced release strategies, the development of advanced organic nanocomposite coatings with MSN and the validation of their anticorrosive performances are reviewed and compared. Through a critical analysis of the literature, the most promising research trends and perspectives to exploit the highly interesting properties of MSN in advanced organic coatings are proposed. Materials science ; Mesoporous silica ; Nanoparticle ; Nanocomposite ; Corrosion ; Mesoporous material ; Smart material ; Nanotechnology ; Chemical engineering ; Composite material ; Organic chemistry ; Chemistry ; Catalysis Corrosion Behavior and Inhibition ; Conducting polymers and applications ; Transition Metal Oxide Nanomaterials
Authors: Yosi Gilad | Gary Gellerman | David M. Lonard | Bert W. O’Malley
Source: Cancers (review, 2021-02-07)
Field: Materials Science — Biomaterials

Drug Combination in Cancer Treatment—From Cocktails to Conjugated Combinations It is well recognized today that anticancer drugs often are most effective when used in combination. However, the establishment of chemotherapy as key modality in clinical oncology began with sporadic discoveries of chemicals that showed antiproliferative properties and which as a first attempt were used as single agents. In this review we describe the development of chemotherapy from its origins as a single drug treatment with cytotoxic agents to polydrug therapy that includes targeted drugs. We discuss the limitations of the first chemotherapeutic drugs as a motivation for the establishment of combined drug treatment as standard practice in spite of concerns about frequent severe, dose limiting toxicities. Next, we introduce the development of targeted treatment as a concept for advancement within the broader field of small-molecule drug combination therapy in cancer and its accelerating progress that was boosted by recent scientific and technological progresses. Finally, we describe an alternative strategy of drug combinations using drug-conjugates for selective delivery of cytotoxic drugs to tumor cells that potentiates future improvement of drug combinations in cancer treatment. Overall, in this review we outline the development of chemotherapy from a pharmacological perspective, from its early stages to modern concepts of using targeted therapies for combinational treatment. Drug ; Medicine ; Chemotherapy ; Drug development ; Combination therapy ; Limiting ; Pharmacology ; Cancer ; Anticancer drug ; Cancer treatment ; Targeted therapy ; Combination chemotherapy ; Intensive care medicine ; Oncology ; Internal medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Protein Degradation and Inhibitors
Authors: Yong X. Gan
Source: C – Journal of Carbon Research (article, 2021-04-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Activated Carbon from Biomass Sustainable Sources Biomass wastes are abundant around us. They are renewable and inexpensive. Product manufacturing from renewable resources has caught increasing interest recently. Activated carbon preparation from biomass resources, including various trees, leaves, plant roots, fruit peels, and grasses, is a good example. In this paper, an overview of activated carbon production from biomass resources will be given. The first part will be on the processing technologies. The second part will focus on the carbon activation methods. The third part will introduce the biomass resources. The fourth part will be on surface modification of activated carbon through the addition of various components. Finally, the development of product applications will be discussed with an emphasis on adsorption, filtration, water purification, energy conversions, and energy storage. Biomass (ecology) ; Renewable energy ; Activated carbon ; Environmental science ; Carbon fibers ; Renewable resource ; Pulp and paper industry ; Adsorption ; Waste management ; Process engineering ; Chemistry ; Computer science ; Engineering ; Agronomy ; Ecology ; Biology Supercapacitor Materials and Fabrication ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Man Hoi Wong | Oliver Bierwagen | Robert Kaplar | Hitoshi Umezawa
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2021-12-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrawide-bandgap semiconductors: An overview Materials science ; Semiconductor ; Doping ; Diamond ; Nanotechnology ; Engineering physics ; Wide-bandgap semiconductor ; Band gap ; Gallium nitride ; Optoelectronics ; Substrate (aquarium) ; Metallurgy Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; ZnO doping and properties
Authors: Zhengguo Wu | Lansheng Wei | Shuwei Tang | Yutong Xiong | Xiaoqian Qin | Jiwen Luo | Jiawei Fang | Xiaoying Wang
Source: ACS Nano (review, 2021-12-06)
Field: Materials Science — Materials Chemistry

Recent Progress in Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> MXene-Based Flexible Pressure Sensors The rapid development of consumer electronics, artificial intelligence, and clinical medicine generates an increasing demand for flexible pressure sensors, whose performance depends significantly on sensitive materials with high flexibility and proper conductivity. MXene, a type of 2D nanomaterial, has attracted extensive attention due to its good electrical conductivity, hydrophilicity, and flexibility. The synthesis methods for MXenes make it relatively easy to control their microstructure and surface termination groups. Hence, MXenes can obtain peculiar microstructures and facilely combine with other functional materials, making them promising prospects for use in flexible pressure sensors. In this Review, recent advances in MXenes are summarized, mainly focusing on the synthesis methods and their application in flexible pressure sensors. Finally, the challenges and potential solutions for future development are also discussed. MXenes ; Flexibility (engineering) ; Nanotechnology ; Materials science ; Electronics ; Nanomaterials ; Pressure sensor ; Mechanical engineering ; Engineering ; Electrical engineering MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; 2D Materials and Applications
Authors: Monica Oliva | Guglielmo A. Coppola | Erik V. Van der Eycken | Upendra K. Sharma
Source: Advanced Synthesis & Catalysis (article, 2021-02-15)
Field: Chemistry — Organic Chemistry

Photochemical and Electrochemical Strategies towards Benzylic C−H Functionalization: A Recent Update Abstract Transition metal‐catalysed processes have been widely used for the functionalization of inert C−H bonds. Strategies for the functionalization of the benzylic C−H position having a relatively weak C−H bond (bond dissociation energy∼ 80–90 kcal/mol) differ from the inert aliphatic and aromatic C−H positions with stronger C−H bonds. The recent advances in the direct activation of the benzylic position through the generation of C( sp 3 ) radicals have demonstrated the potential of electrochemistry and photochemistry as a means for constructing new chemical bonds. This review will cover the recent progress of benzylic C−H functionalization through organic radical strategies employing photochemistry and electrochemistry as sustainable tools. In addition, the mechanistic details of the typical reactions have been included which, in turn, will help the researchers to look at this promising area from a different perspective towards new discoveries and often hidden opportunities. magnified image Chemistry ; Surface modification ; Radical ; Electrochemistry ; Photochemistry ; Inert ; Dissociation (chemistry) ; Combinatorial chemistry ; Organic chemistry ; Physical chemistry ; Electrode Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques
Authors: Anass Benayad | Diddo Diddens | Andreas Heuer | Anand Narayanan Krishnamoorthy | M. Maiti | Frédéric Le Cras | Maxime Legallais | Fuzhan Rahmanian | Yuyoung Shin | Helge S. Stein | Martin Winter | Christian Wölke | Peng Yan | Isidora Cekić-Lasković
Source: Advanced Energy Materials (article, 2021-11-09)
Field: Materials Science — Materials Chemistry

High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research Abstract The timely arrival of novel materials plays a key role in bringing advances to society, as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at which novel materials are identified within chemical space. High‐throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes toward identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision‐making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces. Computer science ; Leverage (statistics) ; Pace ; Throughput ; Workflow ; Automation ; Battery (electricity) ; Field (mathematics) ; Reinforcement learning ; Risk analysis (engineering) ; Nanotechnology ; Artificial intelligence ; Systems engineering ; Data science ; Materials science ; Engineering Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Johannes Hoja | Leonardo Medrano Sandonas | Brian G. Ernst | Alvaro Vazquez-Mayagoitia | Robert A. DiStasio | Alexandre Tkatchenko
Source: Scientific Data (article, 2021-02-02)
Field: Materials Science — Materials Chemistry

QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for ≈4.2 million equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures-comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers)-as well as 100 non-equilibrium structural variations thereof to reach a total of ≈4.2 million molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties. Polarizability ; Chemical space ; Molecule ; Organic molecules ; Enantiomer ; Dipole ; Small molecule ; Sampling (signal processing) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Jaber Rezaei Mianroodi | Nima H. Siboni | Dierk Raabe
Source: npj Computational Materials (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials Abstract We propose a deep neural network (DNN) as a fast surrogate model for local stress calculations in inhomogeneous non-linear materials. We show that the DNN predicts the local stresses with 3.8% mean absolute percentage error (MAPE) for the case of heterogeneous elastic media and a mechanical contrast of up to factor of 1.5 among neighboring domains, while performing 103 times faster than spectral solvers. The DNN model proves suited for reproducing the stress distribution in geometries different from those used for training. In the case of elasto-plastic materials with up to 4 times mechanical contrast in yield stress among adjacent regions, the trained model simulates the micromechanics with a MAPE of 6.4% in one single forward evaluation of the network, without any iteration. The results reveal an efficient approach to solve non-linear mechanical problems, with an acceleration up to a factor of 8300 for elastic-plastic materials compared to typical solvers. Solver ; Micromechanics ; Contrast (vision) ; Artificial neural network ; Mean absolute percentage error ; Computer science ; Stress (linguistics) ; Acceleration ; Linear elasticity ; Algorithm ; Artificial intelligence ; Mathematics ; Mathematical optimization ; Structural engineering ; Finite element method ; Physics ; Composite number ; Engineering ; Classical mechanics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Hemalatha Parangusan | Jolly Bhadra | Noora Al‐Thani
Source: Emergent Materials (review, 2021-04-27)
Field: Materials Science — Materials Chemistry

A review of passivity breakdown on metal surfaces: influence of chloride- and sulfide-ion concentrations, temperature, and pH Abstract Metals, including austenitic steels and alloys, have been extensively applied in industrial and engineering applications. Passive films on metal surfaces are very important for corrosion protection. However, localized attack, such as passive film breakdown and the initiation of pits, is found upon exposure of such metals to aggressive ion-containing environments, leading to material failure and prominent adverse economic and safety concerns. For several decades, the mechanism of passivity breakdown and pit nucleation during pitting corrosion has been widely studied. The present article provides a detailed review of passive film breakdown on metal surfaces and the effects of complicated conditions, such as chloride- and sulfide-ion concentrations, temperature, and solution pH, on passivity breakdown. The possible mechanism for passivity breakdown is reviewed and discussed. The composition, structure, and electronic properties of passive layers are of conclusive importance to understand the leading corrosion mechanism, and they have been investigated with different techniques. Furthermore, we aim to present the structure, chemical composition, and electronic properties of passive films on metal surfaces by using X-ray photoelectron spectroscopy and energy-dispersive spectroscopy. Additionally, the surface morphology of passive films is analyzed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy (AFM) techniques. Finally, the effect of chloride- and sulfide-ion concentrations, pH, and temperature on passivity breakdown is discussed in detail. Passivity ; Sulfide ; Corrosion ; Materials science ; X-ray photoelectron spectroscopy ; Nucleation ; Metal ; Chloride ; Scanning electron microscope ; Transmission electron microscopy ; Pitting corrosion ; Metallurgy ; Chemical engineering ; Analytical Chemistry (journal) ; Composite material ; Chemistry ; Nanotechnology ; Environmental chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Wenchao Lu | Milka Kostić | Tinghu Zhang | Jianwei Che | Matthew P. Patricelli | Lyn H. Jones | Edward T. Chouchani | Nathanael S. Gray
Source: RSC Chemical Biology (review, 2021-01-01)
Field: Chemistry — Organic Chemistry

Fragment-based covalent ligand discovery Covalent fragment-based ligand discovery greatly facilitates the discovery of useful fragments for drug discovery and helps unveil chemical-tractable biological targets in native biological systems. Drug discovery ; Fragment (logic) ; Covalent bond ; Ligand (biochemistry) ; Computational biology ; Ligand efficiency ; Chemistry ; Combinatorial chemistry ; Computer science ; Biology ; Biochemistry ; Receptor ; Algorithm ; Organic chemistry Click Chemistry and Applications ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: A. Wang | Ryan Kingsbury | Matthew J. McDermott | Matthew K. Horton | Anubhav Jain | Shyue Ping Ong | Shyam Dwaraknath | Kristin A. Persson
Source: Scientific Reports (article, 2021-07-29)
Field: Materials Science — Materials Chemistry

A framework for quantifying uncertainty in DFT energy corrections In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability. Density functional theory ; Stability (learning theory) ; Work (physics) ; Statistical physics ; Phase diagram ; Computer science ; Energy (signal processing) ; Scheme (mathematics) ; Phase (matter) ; Thermodynamics ; Applied mathematics ; Computational chemistry ; Mathematics ; Chemistry ; Physics ; Statistics ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Thermal and Kinetic Analysis