jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K. R. Sumesh | K. Kanthavel
Source: Journal of Industrial Textiles (article, 2020-06-06)
Field: Materials Science — Polymers and Plastics

Optimizing various parameters influencing mechanical properties of banana/coir natural fiber composites using grey relational analysis and artificial neural network models The natural fibers of banana and coir from southern part of India was taken as the reinforcement material. Taguchi-based grey relational analysis was used for optimizing the factors influencing tensile, flexural and impact strength of banana/coir composites. Banana wt%, coir wt%, alkali treatment %, pressure and temperature during compression moulding process were the five major factors used for optimizing the mechanical strengths of epoxy-based composites. Grey relational analysis observed 20% banana/15% coir/3% alkali treatment/16 MPa pressure and 100°C temperature combination having the maximum mechanical properties in 16 trial combinations. The signal-to-noise ratio table of grey relational analysis showed combination with 20% banana /15% coir/5% alkali treatment/16 MPa pressure and 100°C temperature having optimized mechanical properties. Banana has the highest rank in improving the mechanical strength of natural composites, followed by coir, alkali treatment %, pressure and temperature. The graph between actual and artificial neural network computed results observed high correlation, proving that network topology of 5-3-1 with five factors, three hidden nodes and one response is the best method for analyzing the parameters in mechanical strength. Hybrid fiber composites with NaOH treatment showed hackles in interface with improved interfacial bonding during SEM analysis. Coir ; Materials science ; Grey relational analysis ; Composite material ; Ultimate tensile strength ; Taguchi methods ; Flexural strength ; Epoxy ; Natural fiber ; Fiber ; Mathematics Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Jun Zhang | Vassiliki‐Alexandra Glezakou
Source: International Journal of Quantum Chemistry (article, 2020-11-21)
Field: Materials Science — Materials Chemistry

Global optimization of chemical cluster structures: Methods, applications, and challenges Abstract Chemical clusters are relevant to many applications in catalysis, separations, materials, and energy sciences. Experimentally, the structure of clusters is difficult to determine, but it is very important in understanding their chemistry and properties. Computational methods can be used to examine cluster structure, however finding the most stable structure is not simple, particularly as the cluster size increases. Global optimization techniques have long been used to tackle the problem of the most stable structure, but such approaches would have to look for a global minimum, while sampling local minima over the whole potential energy surface as well. In this review, the state‐of‐the‐art theory of global optimization theory is summarized. First, the definition, significance, relation to experiments, and a brief history of global optimization is presented. We then discuss, in more detail, three versatile global optimization methods: the basin hopping, the artificial bee colony algorithm, and the genetic algorithm. We close with some representative application examples of global optimization of clusters since 2016 and the challenges, open questions and opportunities in this field. Maxima and minima ; Global optimization ; Cluster (spacecraft) ; Computer science ; Potential energy surface ; Field (mathematics) ; Simple (philosophy) ; Mathematical optimization ; Algorithm ; Chemistry ; Mathematics ; Molecule Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Advanced Chemical Physics Studies
Authors: Simão M. João | Miša Anđelković | Lucian Covaci | Tatiana G. Rappoport | J. M. Viana Parente Lopes | Aires Ferreira
Source: Royal Society Open Science (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

KITE: high-performance accurate modelling of electronic structure and response functions of large molecules, disordered crystals and heterostructures We present KITE, a general purpose open-source tight-binding software for accurate real-space simulations of electronic structure and quantum transport properties of large-scale molecular and condensed systems with tens of billions of atomic orbitals (<i>N</i> ∼ 10<sup>10</sup>). KITE's core is written in C++, with a versatile Python-based interface, and is fully optimized for shared memory multi-node CPU architectures, thus scalable, efficient and fast. At the core of KITE is a seamless spectral expansion of lattice Green's functions, which enables large-scale calculations of generic target functions with uniform convergence and fine control over energy resolution. Several functionalities are demonstrated, ranging from simulations of local density of states and photo-emission spectroscopy of disordered materials to large-scale computations of optical conductivity tensors and real-space wave-packet propagation in the presence of magneto-static fields and spin-orbit coupling. On-the-fly calculations of real-space Green's functions are carried out with an efficient domain decomposition technique, allowing KITE to achieve <i>nearly ideal linear scaling</i> in its multi-threading performance. Crystalline defects and disorder, including vacancies, adsorbates and charged impurity centres, can be easily set up with KITE's intuitive interface, paving the way to user-friendly large-scale quantum simulations of equilibrium and non-equilibrium properties of molecules, disordered crystals and heterostructures subject to a variety of perturbations and external conditions. Computational physics ; Physics ; Computational science ; Materials science ; Computer science Electronic and Structural Properties of Oxides ; Quantum and electron transport phenomena ; 2D Materials and Applications
Authors: Wérickson Fortunato de Carvalho Rocha | Charles B. Prado | Nikša Blonder
Source: Molecules (review, 2020-07-02)
Field: Chemistry — Analytical Chemistry

Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction. Chemometrics ; Artificial neural network ; Computer science ; Field (mathematics) ; Process (computing) ; Machine learning ; Artificial intelligence ; Product (mathematics) ; Linear model ; Support vector machine ; Data mining ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Xiao Lin | Zifei Liu | Tobias Stauber | G. Gómez-Santos | Fei Gao | Hongsheng Chen | Baile Zhang | Tony Low
Source: Physical Review Letters (article, 2020-08-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral Plasmons with Twisted Atomic Bilayers van der Waals heterostructures of atomically thin layers with rotational misalignments, such as twisted bilayer graphene, feature interesting structural moiré superlattices. Because of the quantum coupling between the twisted atomic layers, light-matter interaction is inherently chiral; as such, they provide a promising platform for chiral plasmons in the extreme nanoscale. However, while the interlayer quantum coupling can be significant, its influence on chiral plasmons still remains elusive. Here we present the general solutions from full Maxwell equations of chiral plasmons in twisted atomic bilayers, with the consideration of interlayer quantum coupling. We find twisted atomic bilayers have a direct correspondence to the chiral metasurface, which simultaneously possesses chiral and magnetic surface conductivities, besides the common electric surface conductivity. In other words, the interlayer quantum coupling in twisted van der Waals heterostructures may facilitate the construction of various (e.g., bi-anisotropic) atomically-thin metasurfaces. Moreover, the chiral surface conductivity, determined by the interlayer quantum coupling, determines the existence of chiral plasmons and leads to a unique phase relationship (i.e., ±π/2 phase difference) between their transverse-electric (TE) and transverse-magnetic (TM) wave components. Importantly, such a unique phase relationship for chiral plasmons can be exploited to construct the missing longitudinal spin of plasmons, besides the common transverse spin of plasmons. van der Waals force ; Condensed matter physics ; Plasmon ; Surface plasmon ; Coupling (piping) ; Materials science ; Phase (matter) ; Quantum ; Spin (aerodynamics) ; Quasiparticle ; Heterojunction ; Physics ; Quantum mechanics ; Optoelectronics ; Molecule Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Topological Materials and Phenomena
Authors: Panagiοtis Tsakanikas | Apostolos Karnavas | Efstathios Ζ. Panagou | George‐John E. Nychas
Source: Scientific Reports (article, 2020-07-08)
Field: Chemistry — Analytical Chemistry

A machine learning workflow for raw food spectroscopic classification in a future industry Over the years, technology has changed the way we produce and have access to our food through the development of applications, robotics, data analysis, and processing techniques. The implementation of these approaches by the food industry ensure quality and affordability, reducing at the same time the costs of keeping the food fresh and increase productivity. A system, as the one presented herein, for raw food categorization is needed in future food industries to automate food classification according to type, the process of algorithm approaches that will be applied to every different food origin and also for serving disabled people. The purpose of this work was to develop a machine learning workflow based on supervised PLS regression and SVM classification, towards automated raw food categorization from FTIR. The system exhibited high efficiency in multi-class classification of 7 different types of raw food. The selected food samples, were diverse in terms of storage conditions (temperature, storage time and packaging), while the variability within each food was also taken into account by several different batches; leading in a classifier able to embed this variation towards increased robustness and efficiency, ready for real life applications targeting to the digital transformation of the food industry. Workflow ; Computer science ; Machine learning ; Food industry ; Artificial intelligence ; Food processing ; Support vector machine ; Categorization ; Classifier (UML) ; Robustness (evolution) ; Raw data ; Database ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Hongguang Zhou | Youlin Tan | Wei Gao | Yue Zhang | Yanmei Yang
Source: Scientific Reports (article, 2020-09-30)
Field: Materials Science — Materials Chemistry

Selective nitrate removal from aqueous solutions by a hydrotalcite-like absorbent FeMgMn-LDH FeMgMn-LDH, a type of potential environmental remediation material, has been synthesized via a co-precipitation method, and its adsorption characteristics for nitrate were investigated in this study. It's shown that the prepared FeMgMn-LDH is a promising adsorbent for anions removal, which has high buffer capacity (final pH remained between 9 and 10) and high reversibility, and can remove nitrate ions selectively though an anion-sieve effect. The maximum amount of nitrate adsorption is 10.56 N-mg g<sup>-1</sup> at 25 ℃. The removal rate of nitrate ions can reach 86.26% with the adsorbent dose of 5 g/L in a real water. The competition order of coexisting anions on nitrate adsorption by FeMgMn-LDH is CO<sub>3</sub><sup>2-</sup> > PO<sub>4</sub><sup>3-</sup> > SO<sub>4</sub><sup>2-</sup>. The negative values of ΔG<sup>0</sup> (from - 27.796 to - 26.426 kJ mol<sup>-1</sup>) and ΔH<sup>0</sup> (- 6.678 kJ mol<sup>-1</sup>) indicate that the nitrate adsorption process on the FeMgMn-LDH is spontaneous and exothermic. The main adsorption mechanisms of nitrate removal from aqueous solutions by FeMgMn-LDH are electrostatic attraction and ion exchange. Adsorption ; Nitrate ; Chemistry ; Aqueous solution ; Hydrotalcite ; Inorganic chemistry ; Ion exchange ; Ion ; Nuclear chemistry ; Catalysis ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Phosphorus and nutrient management ; Esophageal and GI Pathology
Authors: Jeffrey D. St. Denis | Richard J. Hall | Christopher W. Murray | Tom D. Heightman | David C. Rees
Source: RSC Medicinal Chemistry (review, 2020-12-24)
Field: Chemistry — Organic Chemistry

Fragment-based drug discovery: opportunities for organic synthesis This Review describes the increasing demand for organic synthesis to facilitate fragment-based drug discovery (FBDD), focusing on polar, unprotected fragments. In FBDD, X-ray crystal structures are used to design target molecules for synthesis with new groups added onto a fragment <i>via</i> specific growth vectors. This requires challenging synthesis which slows down drug discovery, and some fragments are not progressed into optimisation due to synthetic intractability. We have evaluated the output from Astex's fragment screenings for a number of programs, including urokinase-type plasminogen activator, hematopoietic prostaglandin D2 synthase, and hepatitis C virus NS3 protease-helicase, and identified fragments that were not elaborated due, in part, to a lack of commercially available analogues and/or suitable synthetic methodology. This represents an opportunity for the development of new synthetic research to enable rapid access to novel chemical space and fragment optimisation. Fragment (logic) ; Drug discovery ; Organic synthesis ; Elaboration ; Chemistry ; Combinatorial chemistry ; Stereochemistry ; Computer science ; Organic chemistry ; Biochemistry ; Algorithm ; Philosophy ; Humanities ; Catalysis Synthesis and Catalytic Reactions ; Chemical Synthesis and Analysis ; Click Chemistry and Applications
Authors: Danica Jović | Vesna Jaćević | Kamil Kuča | Ivana Borišev | Jasminka Mrdjanović | Danijela Petrović | Mariana Seke | Aleksandar Djordjević
Source: Nanomaterials (review, 2020-07-31)
Field: Materials Science — Materials Chemistry

The Puzzling Potential of Carbon Nanomaterials: General Properties, Application, and Toxicity Being a member of the nanofamily, carbon nanomaterials exhibit specific properties that mostly arise from their small size. They have proved to be very promising for application in the technical and biomedical field. A wide spectrum of use implies the inevitable presence of carbon nanomaterials in the environment, thus potentially endangering their whole nature. Although scientists worldwide have conducted research investigating the impact of these materials, it is evident that there are still significant gaps concerning the knowledge of their mechanisms, as well as the prolonged and chronic exposure and effects. This manuscript summarizes the most prominent representatives of carbon nanomaterial groups, giving a brief review of their general physico-chemical properties, the most common use, and toxicity profiles. Toxicity was presented through genotoxicity and the activation of the cell signaling pathways, both including <i>in vitro</i> and <i>in vivo</i> models, mechanisms, and the consequential outcomes. Moreover, the acute toxicity of fullerenol, as one of the most commonly investigated members, was briefly presented in the final part of this review. Thinking small can greatly help us improve our lives, but also obliges us to deeply and comprehensively investigate all the possible consequences that could arise from our pure-hearted scientific ambitions and work. Nanotechnology ; Nanomaterials ; Biochemical engineering ; Toxicity ; Carbon fibers ; Chemistry ; Computer science ; Materials science ; Engineering Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanoparticles: synthesis and applications
Authors: Karina Ovejero Paredes | Diana Díaz‐García | Victoria García‐Almodóvar | Laura Lozano Chamizo | Marzia Marciello | Miguel Díaz‐Sánchez | Sanjiv Prashar | Santiago Gómez‐Ruiz | Marco Filice
Source: Cancers (article, 2020-01-12)
Field: Chemistry — Organic Chemistry

Multifunctional Silica-Based Nanoparticles with Controlled Release of Organotin Metallodrug for Targeted Theranosis of Breast Cancer Three different multifunctional nanosystems based on the tethering onto mesoporous silica nanoparticles (MSN) of different fragments such as an organotin-based cytotoxic compound Ph<sub>3</sub>Sn{SCH<sub>2</sub>CH<sub>2</sub>CH<sub>2</sub>Si(OMe)<sub>3</sub>} (MSN-AP-Sn), a folate fragment (MSN-AP-FA-Sn), and an enzyme-responsive peptide able to release the metallodrug only inside cancer cells (MSN-AP-FA-PEP-S-Sn), have been synthesized and fully characterized by applying physico-chemical techniques. After that, an in vitro deep determination of the therapeutic potential of the achieved multifunctional nanovectors was carried out. The results showed a high cytotoxic potential of the MSN-AP-FA-PEP-S-Sn material against triple negative breast cancer cell line (MDA-MB-231). Moreover, a dose-dependent metallodrug-related inhibitory effect on the migration mechanism of MDA-MB-231 tumor cells was shown. Subsequently, the organotin-functionalized nanosystems have been further modified with the NIR imaging agent Alexa Fluor 647 to give three different theranostic silica-based nanoplatforms, namely, MSN-AP-Sn-AX (AX-1), MSN-AP-FA-Sn-AX (AX-2), and MSN-AP-FA-PEP-S-Sn-AX (AX-3). Their in vivo potential as theranostic markers was further evaluated in a xenograft mouse model of human breast adenocarcinoma. Owing to the combination of the receptor-mediated site targeting and the specific fine-tuned release mechanism of the organotin metallodrug, the nanotheranostic drug MSN-AP-FA-PEP-S-Sn-AX (AX-3) has shown targeted diagnostic ability in combination with enhanced therapeutic activity by promoting the inhibition of tumor growth with reduced hepatic and renal toxicity upon the repeated administration of the multifunctional nanodrug. Chemistry ; Mesoporous silica ; In vivo ; Cytotoxicity ; Triple-negative breast cancer ; Alexa Fluor ; In vitro ; Cancer cell ; Peptide ; Stereochemistry ; Cancer research ; Breast cancer ; Nanotechnology ; Biophysics ; Cancer ; Biochemistry ; Mesoporous material ; Materials science ; Catalysis Organometallic Compounds Synthesis and Characterization ; Bone health and treatments ; Ferrocene Chemistry and Applications
Authors: Hongzhi Zhou | Yida Zhao | Weijian Tao | Yujie Li | Qiaohui Zhou | Haiming Zhu
Source: ACS Nano (article, 2020-03-17)
Field: Materials Science — Materials Chemistry

Controlling Exciton and Valley Dynamics in Two-Dimensional Heterostructures with Atomically Precise Interlayer Proximity Two-dimensional (2D) materials and heterostructures with strong excitonic effect and spin/valley properties have emerged as an exciting platform for optoelectronic and spin/valleytronic applications. There, precise control of the exciton transformation process (including intralayer to interlayer exciton transition and recombination) and valley polarization process <i>via</i> structural tuning is crucial but remains largely unexplored. Here, using hexagonal boron nitride (BN) as an intermediate layer, we show the fine-tuning of exciton and valley dynamics in 2D heterostructures with atomic precision. Both interfacial electron and hole transfer rates decrease exponentially with increasing BN thickness, which can be well-described with quantum tunneling model. The increased spatial separation with BN intercalation weakens the electron-hole Coulomb interaction and significantly prolongs the interlayer exciton population and valley polarization lifetimes in van der Waals (vdW) heterostructures. For example, WSe<sub>2</sub>/WS<sub>2</sub> heterostructures with monolayer BN intercalation exhibit a hole valley polarization lifetime of ∼60 ps at room temperature, which is approximately threefold and 3 orders of magnitude longer than that in WSe<sub>2</sub>/WS<sub>2</sub> heterobilayer without BN and WSe<sub>2</sub> monolayer, respectively. Considering a large family of layered materials, this study suggests a general approach to tailor and optimize exciton and valley properties in vdW heterostructures with atomic precision. Exciton ; Heterojunction ; Monolayer ; Trion ; van der Waals force ; Condensed matter physics ; Materials science ; Quantum tunnelling ; Polarization (electrochemistry) ; Electron ; Coulomb ; Nanotechnology ; Chemistry ; Physics 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Seema Gupta | Indu Singh | Ashwani Kumar Sharma | Pradeep Kumar
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-05-29)
Field: Materials Science — Biomaterials

Ultrashort Peptide Self-Assembly: Front-Runners to Transport Drug and Gene Cargos The translational therapies to promote interaction between cell and signal come with stringent eligibility criteria. The chemically defined, hierarchically organized, and simpler yet blessed with robust intermolecular association, the peptides, are privileged to make the cut-off for sensing the cell-signal for biologics delivery and tissue engineering. The signature service and insoluble network formation of the peptide self-assemblies as hydrogels have drawn a spell of research activity among the scientists all around the globe in the past decades. The therapeutic peptide market players are anticipating promising growth opportunities due to the ample technological advancements in this field. The presence of the other organic moieties, enzyme substrates and well-established protecting groups like Fmoc and Boc etc., bring the best of both worlds. Since the large sequences of peptides severely limit the purification and their isolation, this article reviews the account of last 5 years' efforts on novel approaches for formulation and development of single molecule amino acids, ultra-short peptide self-assemblies (di-and tri-peptides only) and their derivatives as drug/gene carriers and tissue-engineering systems. Peptide ; Self-healing hydrogels ; Drug delivery ; Nanotechnology ; Molecular engineering ; Signal peptide ; Tissue engineering ; Chemistry ; Combinatorial chemistry ; Computational biology ; Materials science ; Biochemistry ; Peptide sequence ; Gene ; Biology ; Organic chemistry ; Genetics Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques