jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: R. Anand | K. Monisha
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-10-21)
Field: Materials Science — General Materials Science

Comparative Study of SWCNT Reinforced E-Glass/Epoxy Composite with Bidirectional E-Glass/Epoxy Composite Plate The main aim of this project is to compare the composite material using carbon nanotubes. In current sensation composite materials are very important in aerospace automotive structural applications. Carbon nanotube gives an additional strength to the material which is going too compared with the composite material made up of resin and fibres E-glass fibres reinforced composite plates are fabricated using hand layup method. These plates are tested under low velocity impact in drop weight at three distinct impact energies, followed by in plane compression The values are examined and the methods to enhance the strength of the composite plate will be discussed, this research experimental and numerical test result will be analyzed to determine the damage occurred in the material and the strength to withstand the weight applied in the material. Epoxy ; Composite number ; Composite material ; Materials science ; Composite epoxy material ; Glass fiber Material Properties and Applications ; Epoxy Resin Curing Processes ; Mechanical Behavior of Composites
Authors: Nisa SİPAHİ | Ekrem AKBULUT | Aysun SARAÇ | Tuğba Zengin
Source: Düzce Üniversitesi Bilim ve Teknoloji Dergisi (article, 2022-04-03)
Field: Chemistry — Organic Chemistry

Farklı Yapıdaki Kumaşlara Antimikrobiyal Apre Uygulaması ve Etkinliklerinin Karşılaştırılması Hastalıklardan korunmada tekstil ürünlerin büyük rolü bulunmaktadır. Tekstil materyaller günlük hayatın birçok yerinde karşımıza çıkmaktadır. Özellikle son iki yıldır yaşanan covid-19 pandemisi tekstilin önemine daha da dikkat çekmiştir. Bununla birlikte “tekstil bizi salgınlardan koruyabilir mi?” sorusunu da gündeme getirmiştir. Bakterilerle dolu bir çevremiz olması, virüslerin bakterilere tutunarak saçılması ve nozokomiyal hastalıkların son yıllarda artış göstermesi, antimikrobiyal tekstil üretimine gereksinim olduğunu düşündürmektedir. Bu çalışmada farklı yapıdaki kumaşlara gümüş ve kuarterner amonyum tuz (QAS) apre uygulanması ve antimikrobiyal etkinliklerinin karşılaştırılması amaçlanmıştır. Kumaşlardan birisi %100 pamuk liflerden diğeri ise %100 poliester liflerden dokunmuştur. Uygulanan antimikrobiyal bitim işlemleri sonrası etkinlikleri AATCC 100 test metoduna göre belirlenmiştir. Kontrol olarak apre uygulanmamış kumaşlar aynı teste tabi tutulmuştur; ancak herhangi bir mikrobiyal inhibisyona rastlanmamıştır. Buna göre tüm gümüş apreli pamuk kumaşlarda etkinin diğerlerine oranla daha yüksek olduğu tespit edilmiştir. En yüksek inhibisyon oranı %99,03 ±0,49 olarak gümüş apreli pamuk kumaşta tespit edilmiştir. En düşük inhibisyon oranı ise QAS apreli poliester kumaşta %59,43 ±1,87 olarak bulunmuştur. Ayrıca kumaşların gram pozitif bakterilere karşı daha etkin olduğu görülmüştür. Aynı zamanda uygulanan apre işleminin kumaşlarda renk değişimi, pilling (boncuklanma) ve hidrofiliteye neden olup olmadığı da test edilmiş ve herhangi bir etki olmadığı görülmüştür. Sonuç olarak bu çalışmada farklı yapıdaki kumaşlara uygulanan antimikrobiyal bitim işlemlerinin yüksek etkinlik gösterdiği görülmüştür. Çeşitli enfeksiyon hastalıklarındaki eradikasyon çalışmalarında, günümüz pandemisi ve hastane enfeksiyonları ile mücadelede antimikrobiyal tekstil sektörünün oldukça büyük bir yere sahip olduğu ve geliştirilmesi gerektiği düşünülmektedir. Physics Antimicrobial agents and applications ; Contact Dermatitis and Allergies ; Healthcare and Environmental Waste Management
Authors: Marko Bek | Alexandra Aulova | Alen Oseli | Igor Emri
Source: Anali PAZU (article, 2022-05-09)
Field: Materials Science — General Materials Science

Nova generacija udarne in vibracijske izolacije na osnovi visokotlačne omrežne tehnologije The article reviews the know-why and the know-how of an invention based on the patented dissipative granular high-pressure technology. It was found that by proper selection of damping material and hydrostatic pressure to which material is exposed during the loading, one can match material maximum damping properties with the frequency or rate of the applied loading. In this way one can fully utilize damping characteristics of the selected material and maximize the energy absorption properties of a damper. Using this unique potential of the dissipative granular high-pressure technology one can build ultimate damping elements that surpass the existing damping elements for sever orders of magnitude. Applications of such damping elements include for instance supports for industrial machines to damp vibrations, fundaments in building constructions to reduce susceptibility to earthquake damage and resonance, as well as trains and railway tracks to reduce vibration during travel, and to improve passive car safety in road transportation. This article reviews phenomenological description of the time-dependent response of polymeric material when excited by impact- or vibrational loading needed in development of the new generation impactand vibration isolation. Physics ; Humanities ; Art Material Properties and Applications ; Life Cycle Costing Analysis ; Structural Analysis of Composite Materials
Authors: Eric R. Carlson | J. R. Smith
Source:  (preprint, 2022-07-29)
Field: Materials Science — Materials Chemistry

A ±0 &lt;p&gt;This paper presents a step-up DC-DC converter that uses a stepwise gate-drive technique to reduce the power FET gate-drive energy by 82%, allowing positive efficiency down to an input voltage of ±0.5 mV—the lowest input voltage ever achieved for a DC-DC converter as far as we know. Below ±0.5 mV the converter automatically hibernates, reducing quiescent power consumption to just 255 pW. The converter has an efficiency of 63% at ±1 mV and 84% at ±6 mV. The input impedance is programmable from 1 Ω to 600 Ω to achieve maximum power extraction. A novel delay line circuit controls the stepwise gate-drive timing, programmable input impedance, and hibernation behavior. Bipolar input voltage is supported by using a flyback converter topology with two secondary windings. A generated power good signal enables the load when the output voltage has charged above 2.7 V and disables when the output voltage has discharged below 2.5 V. The DC-DC converter was used in a thermoelectric energy harvesting system that effectively harvests energy from small indoor temperature fluctuations of less than 1°C. Also, an analytical model with unprecedented accuracy of the stepwise gate-drive energy is presented. &lt;/p&gt; Flyback converter ; Electrical engineering ; Boost converter ; Forward converter ; Voltage ; Buck–boost converter ; Ćuk converter ; Engineering ; Electronic engineering ; Topology (electrical circuits) ; Control theory (sociology) ; Computer science Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Energy Harvesting in Wireless Networks
Authors: Eric R. Carlson | J. R. Smith
Source:  (preprint, 2022-07-29)
Field: Materials Science — Materials Chemistry

A ±0.5-mV-Minimum-Input DC-DC Converter with Stepwise Adiabatic Gate-Drive and Efficient Timing Control for Thermoelectric Energy Harvesting &lt;p&gt;This paper presents a step-up DC-DC converter that uses a stepwise gate-drive technique to reduce the power FET gate-drive energy by 82%, allowing positive efficiency down to an input voltage of ±0.5 mV—the lowest input voltage ever achieved for a DC-DC converter as far as we know. Below ±0.5 mV the converter automatically hibernates, reducing quiescent power consumption to just 255 pW. The converter has an efficiency of 63% at ±1 mV and 84% at ±6 mV. The input impedance is programmable from 1 Ω to 600 Ω to achieve maximum power extraction. A novel delay line circuit controls the stepwise gate-drive timing, programmable input impedance, and hibernation behavior. Bipolar input voltage is supported by using a flyback converter topology with two secondary windings. A generated power good signal enables the load when the output voltage has charged above 2.7 V and disables when the output voltage has discharged below 2.5 V. The DC-DC converter was used in a thermoelectric energy harvesting system that effectively harvests energy from small indoor temperature fluctuations of less than 1°C. Also, an analytical model with unprecedented accuracy of the stepwise gate-drive energy is presented. &lt;/p&gt; Flyback converter ; Electrical engineering ; Boost converter ; Forward converter ; Ćuk converter ; Buck–boost converter ; Voltage ; Topology (electrical circuits) ; Electronic engineering ; Engineering ; Control theory (sociology) ; Computer science Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Energy Harvesting in Wireless Networks
Authors: Réka Alexandra Pózmán | Richárd Cselkó
Source:  (article, 2022-01-01)
Field: Materials Science — Biomaterials

Maszkok szűrőképességének vizsgálata és növelése electrospinning technológia használatával 2019-ben jelent meg a Covid-19, ami azóta is érzékelteti hatását az életünkre. A vírus számtalan módon terjedhet, leginkább a kilélegzett, fertőzött folyadék részecskék által jut el emberről emberre. Szinte a legelső, és azóta elterjedt védekezési óvintézkedés az orrot és szájat eltakaró maszkok folyamatos használata, amiknek a hatékonysága viszont több paramétertől is függ. Hivatalosan ezeket három nagy kategóriába soroljuk szűrőképesség alapján, mégpedig FFP1, FFP2 és FFP3- ba. Mindhárom a 0,6 mikrométeres és annál nagyobb részecskéket képes szűrni, azonban az FFP1 80%-os, az FFP2 94%-os, míg az FFP3 típusú maszkok mindezt 99%-os hatékonysággal teszik meg. A probléma a textil maszkok megjelenésével indult, melyek szűrőképessége nem sorolható be az előbbi kategóriákba, így megfelelő védelmet sem nyújt viselője számára. A kutatás fő célja ennek a problémának az orvoslása egy általunk készített electrospinning technológiát alkalmazó, nagyfeszültség segítségével nanoszálakat képző berendezés segítségével. COMSOL szimulációval egy modell több esetét is teszteljük, amely során különböző paramétereket vizsgálva keressük az optimális beállításokat. Ilyen vizsgálati paraméter többek között a tűk száma és ezeknek az egymástól való távolsága. Ezzel a lépéssel elérhető, hogy a berendezés magas szűrőhatékonysággal rendelkező nanoszálakból álló réteget hozzon létre, amit laboratóriumi körülmények között tudunk vizsgálni. A tesztelés során előkerülő fontosabb paraméterek még az oldat sűrűsége, az elektródtávolság és a feszültség értéke is. A célunk, hogy olyan szövetből és egy arra applikált poliamid nanoszálas rétegből álló maszkot hozzunk létre, ami mérések alapján jobb szűrkőképességgel rendelkezik, mint a csupán textil anyagú maszkok. Work (physics) ; Mechanical strength ; Investment (military) Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Aerosol Filtration and Electrostatic Precipitation
Authors: C Acosta-Soto | Guido Correa de ARAÚJO | Cristiano Proposal | O Araujo | Juan-Manuel Campos | Castañeda Sedano | Chin Cheah | Cuevas-Cancino Esteva | María De La | Ana Daniel
Source: 2022 IEEE Global Engineering Education Conference (EDUCON) (article, 2022-03-28)
Field: Chemistry — Physical and Theoretical Chemistry

Authors Implementation of hybrid chemistry labs Computer science Various Chemistry Research Topics ; Chemical Safety and Risk Management ; Chemistry and Chemical Engineering
Authors: Minjie Chen
Source:  (preprint, 2022-10-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics &lt;p&gt;This paper applies machine learning to power mag- netics modeling. We first introduce an open-source database – MagNet – which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions, consisting of more than 500,000 data points in its current state. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling the core losses and B–H loops. Machine learning allows multiple factors that may influence the magnetic characteristics to be mod- eled in a unified framework, while provides insights to quantify the complexity of magnetic characteristics and reduce the size of the measurement data required to build a precise model. Neural network models are found to be effective in compressing the measurement data and predicting the material characteristics. The behaviors of a typical power magnetic material (TDK N87) across a wide range of operating conditions (e.g., temperature, waveform, dc-bias) can be well described by a small-scale neural network (204 KB) which is 2,500 times smaller than the raw measured time-series data (512 MB), paving the way for “neural networks as datasheet” to assist power magnetics design. &lt;/p&gt; Datasheet ; Artificial neural network ; Waveform ; Magnet ; Power (physics) ; Computer science ; Data acquisition ; Electronic engineering ; Artificial intelligence ; Engineering ; Electrical engineering ; Voltage ; Physics Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Magnetic properties of thin films
Authors: Morgane Mando
Source: theses.fr (ABES) (article, 2022-03-10)
Field: Chemistry — Organic Chemistry

Organocatalytic enantioselective asymmetric allylic alkylation for quaternary stereocentres construction and aromatic Cope rearrangement study Le développement de nouvelles méthodologies pour accéder à des molécules complexes fait partie des défis majeurs de la chimie organique. Les centres stéréogènes sont des motifs structuraux clés, source de cette complexité. Ce travail décrit la mise au point d’une réaction d’Alkylation Allylique Asymétrique catalytique sur des -aryl--lactones, permettant de construire des stéréocentres quaternaires hautement fonctionnalisés. Cet objectif a pu être atteint grâce à la conception de bases de Lewis originales. Ainsi un grand nombre de lactones chirales possédant soit un centre quaternaire soit un centre quaternaire adjacent à un centre tertiaire ont été préparées avec de bons à très bons rendements et des rapports énantiomériques élevés. Les caractéristiques structurales des composés obtenus ont permis d’effectuer l’étude d’une réaction que l’on peut qualifier « d’oubliée », la réaction de Cope Aromatique, et montrer ainsi son fort potentiel synthétique. Allylic rearrangement ; Lewis acids and bases ; Enantioselective synthesis ; Cope rearrangement ; Alkylation Asymmetric Synthesis and Catalysis ; Organoboron and organosilicon chemistry ; Axial and Atropisomeric Chirality Synthesis
Authors: Caroline Bourgeois
Source: theses.fr (ABES) (preprint, 2022-02-07)
Field: Chemistry — Analytical Chemistry

Mise au point de méthodes pour l’analyse de substances mentionnées dans le règlement Européen REACh et potentiellement présentes dans les process et produits de la filière cuir : formaldéhyde-fluorocarbonés The leather manufacturing needs the addition of chemicals to transform the raw hide into a rot-proof product fitted to its final use. In order to ensure the protection of human health and environment, various regulations restrict the use of these chemicals. Therefore, method developments are needed to get reliable quantification of these substances in leather, a complex matrix. In this work, two examples are introduced: formaldehyde and poly and perfluorinated compounds (fluorocarbons). Formaldehyde has excellent cross-linking properties and is used in the synthesis of retanning resins. However, in final products, formaldehyde may cause an allergic skin reaction. Formaldehyde belongs to the REACh Annex XIV candidate list of very high concern substances and is therefore regulated in case it is present at a level of more than 0.1% in the final article. Since May 2021, a formaldehyde must not exceed 30 mg/kg in leather or textile materials in toys. The evaluation of the currently applicable ISO 17226-1 standard for the quantification of formaldehyde in leather on a very wide range of leathers has shown that it has a bias leading to an overestimation of formaldehyde contents. The first part of this thesis project points out the biases responsible for this overestimation and suggests modifications leading to reliable quantification of formaldehyde, regardless of the manufacturing process. On the other hand, poly and perfluorinated compounds are used in leather finishing step because of their fat and water repellent properties. However, some of these compounds are harmful and persistent in the environment. For example, Annex 1 of the EU Regulation 850/2004 on Persistent Organic Pollutants sets a limit of 1 µg/m² for perfluorooctane sulfonic acid (PFOS) and its derivatives in textiles and coated materials. The second part of this research project aims to increase the number of poly and perfluorinated compounds analysed in leather and to lower their limits of quantification in order to meet the regulations. A complete study of the quantification of these compounds by LC-MS-MS is realised. The study of matrix effects in leather extracts shows these effects are strongly dependent on the manufacturing processes used. Although the addition of a solid phase extraction step allows to avoid such effects in some matrices, it is not always enough to completely eliminate them. Only the addition of labelled internal standards for each compound can eliminate these effects in order to guarantee a reliable quantification of poly and perfluorinated compounds in leather. Formaldehyde ; Process (computing) ; Engineering ; Environmental science ; Biochemical engineering ; Waste management ; Manufacturing engineering ; Process engineering ; Business ; Chemistry ; Computer science ; Organic chemistry Analytical chemistry methods development
Authors: James Andrews | Olga Gkountouna | Estela Blaisten‐Barojas
Source: arXiv (Cornell University) (preprint, 2022-03-01)
Field: Materials Science — Materials Chemistry

Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning Machine learning techniques including neural networks are popular tools for materials and chemical scientists with applications that may provide viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. However, efforts are less abundant for prediction of dynamics. Here we explore the ability of three well established recurrent neural network architectures for forecasting the energetics of a macromolecular polymer-lipid aggregate solvated in ethyl acetate at ambient conditions. Data models generated from recurrent neural networks are trained and tested on nanoseconds-long time series of the intra-macromolecules potential energy and their interaction energy with the solvent generated from Molecular Dynamics and containing half million points. Our exhaustive analyses convey that the three recurrent neural network investigated generate data models with limited capability of reproducing the energetic fluctuations and yielding short or long term energetics forecasts with underlying distribution of points inconsistent with the input series distributions. We propose an in silico experimental protocol consisting on forming an ensemble of artificial network models trained on an ensemble of series with additional features from time series containing pre-clustered time patterns of the original series. The forecast process improves by predicting a band of forecasted time series with a spread of values consistent with the molecular dynamics energy fluctuations span. However, the distribution of points from the band of forecasts is not optimal. Although the three inspected recurrent neural networks were unable of generating single models that reproduce the actual fluctuations of the inspected molecular system energies in thermal equilibrium at the nanosecond scale, the proposed protocol provides useful estimates of the molecular fate Artificial neural network ; Computer science ; Series (stratigraphy) ; Artificial intelligence ; Machine learning ; Molecular dynamics ; Biological system ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Zhilong Liang | Zhiwei Li | Shuo Zhou | Yiwen Sun | Changshui Zhang | Jinying Yuan
Source: arXiv (Cornell University) (preprint, 2022-02-28)
Field: Materials Science — Materials Chemistry

A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility Prediction of material property is a key problem because of its significance to material design and screening. We present a brand-new and general machine learning method for material property prediction. As a representative example, polymer compatibility is chosen to demonstrate the effectiveness of our method. Specifically, we mine data from related literature to build a specific database and give a prediction based on the basic molecular structures of blending polymers and, as auxiliary, the blending composition. Our model obtains at least 75% accuracy on the dataset consisting of thousands of entries. We demonstrate that the relationship between structure and properties can be learned and simulated by machine learning method. Compatibility (geochemistry) ; Property (philosophy) ; Computer science ; Key (lock) ; Polymer ; Artificial intelligence ; Machine learning ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Andrew F. Zahrt | Yiming Mo | Kakasaheb Y. Nandiwale | Ron Shprints | Esther Heid | Klavs F. Jensen
Source: ChemRxiv (preprint, 2022-09-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery of New Electrochemical Reactions The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to invent new electrochemical reactions. To this end, we first develop a novel molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico and the predictions used to identify a number of new reactions of synthetic or mechanistic interest, 80 % of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry. Workflow ; Computer science ; Representation (politics) ; Field (mathematics) ; Artificial intelligence ; Set (abstract data type) ; Biochemical engineering ; In silico ; Cheminformatics ; Organic molecules ; Nanotechnology ; Data science ; Chemistry ; Machine learning ; Molecule ; Materials science ; Database ; Engineering ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques