jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniela Lospinoso | Adriano Colombelli | M. Lomascolo | R. Rella | Maria Grazia Manera
Source: Nanomaterials (article, 2022-01-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-Assembled Metal Nanohole Arrays with Tunable Plasmonic Properties for SERS Single-Molecule Detection Arrays of metal nano-holes have proved to be among of the most promising structures for applications in the field of nano-photonics and optoelectronics. Supporting both localized and propagating surface plasmons resonances, they are characterized by very high versatility thanks to the tunability of these modes, by means of the change of their periodicity, the size of the holes and metal composition. The interaction between different optical features can be exploited to modulate electromagnetic field distribution leading various hot-spots excitations on the metal surfaces. In this work, long range ordered arrays of nano-holes in thin gold films, with different geometrical characteristics, were fabricated by a modified nano-sphere lithography protocol, which allows precise control on holes' dimensions together with the preservation of the order and of the pristine periodicity of the array. An in-depth analysis of the correlation between surface plasmon modes interference and its effect on electromagnetic field distribution is proposed, both by numerical simulations and experimentally. Finally, metal nano-holes arrays are exploited for surface enhanced Raman experiments, evaluating and comparing their performances by the estimation of the enhancement factor. Values close to the single molecule detection are obtained for most of the samples, proving their potentialities in surface enhanced spectroscopy applications. Materials science ; Plasmon ; Nanosphere lithography ; Surface plasmon ; Nanophotonics ; Nano- ; Photonics ; Electromagnetic field ; Lithography ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Nanotechnology ; Optoelectronics ; Field (mathematics) ; Localized surface plasmon ; Raman scattering ; Optics ; Fabrication ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Joshua P. King | Calum S.G. Butler | Stuart W. Prescott | Anna Sokolova | Liliana de Campo | Ashley P. Williams | Rico F. Tabor
Source: Physics of Fluids (article, 2022-07-16)
Field: Chemistry — Organic Chemistry

Exploring shear alignment of concentrated wormlike micelles using rheology coupled with small-angle neutron scattering Wormlike micelles (WLMs) are vital components of many consumer products and industrial fluids, adding a shear-dependent viscous texture through their entanglement in solutions. It is now well accepted from experiments such as coupling rheology and scattering that, similar to many polymer solutions and dispersions of highly anisotropic particles, WLM behavior during shear arises from the alignment of the “worms” with the shear field, resulting in ordering that is rapidly lost in the cessation of shear. Most studies of such systems have been limited to dilute systems that are far below concentrations used industrially and commercially, due to the complexity of analyzing shear-induced many-body effects in high volume fraction dispersions. Here, we explore the shear alignment of concentrated WLM solutions comprising sodium laureth sulfate and cocamidopropyl betaine in 0.38 M aqueous sodium chloride. By analyzing only scattering data at high values of the scattering vector (i.e., correlations at short length scales that are dominant in such concentrated systems), we explore whether useful information can be obtained by naïvely approximating the WLMs as an ensemble of unconnected short rods representing sections of the worms. By taking this reductionist approach to analyzing the obtained two-dimensional scattering patterns from these systems under shear, we find that in this regime, such concentrated worms can be approximated as cylinders that become more aligned with the direction of shear as volume fraction and shear rate increase. Neutron scattering ; Shear (geology) ; Rheology ; Physics ; Shear rate ; Scattering ; Small-angle neutron scattering ; Volume fraction ; Shear thinning ; Static light scattering ; Excluded volume ; Light scattering ; Mechanics ; Polymer ; Optics ; Thermodynamics ; Materials science ; Composite material ; Nuclear magnetic resonance Surfactants and Colloidal Systems ; Material Dynamics and Properties ; NMR spectroscopy and applications
Authors: Rajesh Deka | Andreas Orthaber
Source: Dalton Transactions (review, 2022-01-01)
Field: Chemistry — Organic Chemistry

Carbene chemistry of arsenic, antimony, and bismuth: origin, evolution and future prospects The discovery of the first isolable N-heterocyclic carbene in 1991 ushered in a new era in coordination chemistry. The remarkable bonding properties of carbenes have led to their rapid proliferation as auxiliary ligands for a wide range of transition metals and main group elements. In the case of group 15, while carbene-stabilized nitrogen and phosphorus compounds are extensively studied, the scope of research has shrunk significantly from arsenic to bismuth. This is essentially attributed to the decrease in stability of the C-E bond upon descending the group. Even so, modulating the carbene backbone or introducing alternative synthetic strategies not only alleviates the stability issues but also offers promising results in terms of the bonding and reactivities of these compounds. The purpose of the present perspective is to provide a comprehensive overview of the origins and development of carbene chemistry of arsenic, antimony, and bismuth, as well as to highlight the future prospects of this field. Antimony ; Bismuth ; Arsenic ; Carbene ; Triad (sociology) ; Chemistry ; Perspective (graphical) ; Nanotechnology ; Materials science ; Organic chemistry ; Computer science ; Sociology ; Catalysis ; Social science N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Synthesis and characterization of novel inorganic/organometallic compounds ; Catalytic Cross-Coupling Reactions
Authors: Beata Zygmunt-Kowalska | Kinga Pielichowska | Patrycja Trestka | Magdalena Ziąbka | Monika Kuźnia
Source: Energies (article, 2022-03-10)
Field: Materials Science — Polymers and Plastics

The Effect of Ash Silanization on the Selected Properties of Rigid Polyurethane Foam/Coal Fly Ash Composites According to the assumptions of the European Union, by 2050 it is planned to achieve climate neutrality. For this purpose, a document called the “European Green Deal” was established, which is a set of policies of the European Commission. One of the assumptions is a circular economy that takes into account the use of waste in subsequent production cycles. In order to meet the latest trends in environmentally friendly materials and use of waste in the production of building materials, composites of rigid polyurethane foam with 10 wt.% of waste were produced. Fly ash from coal combustion after modification was used as a filler. Three types of modifications were used: silanization, sieving, and both processes together. The silanization process was carried out for 1 and 2% silane ([3-(2-aminoethylamino)propyl]trimethoxysilane) concentration in relation to the fly ash mass. The sieving was aimed at reaching a fraction with a particle diameter below 75 µm. Six composites with modified fillers were compared and one material containing unchanged fly ash was used as a reference. A comparative analysis was carried out on the basis of surface analysis, thermal stability and physical properties. It turned out that the polyurethane materials modified fly ash silanized with 1% and 2% silane solution proved the best results in performed tests. On the other hand, the polyurethane foam containing sieved ash was characterized by the lowest flammability and the lowest emission of smoke and CO. The use of modified fly ash in technology of polyurethane foams can be a good method of its disposal and can increase the applicability of the composites. Fly ash ; Silanization ; Polyurethane ; Materials science ; Filler (materials) ; Composite material ; Silane ; Waste management Flame retardant materials and properties ; Recycling and utilization of industrial and municipal waste in materials production ; Polymer Foaming and Composites
Authors: Magdalena Osial | Agnieszka Pręgowska
Source: Future Internet (article, 2022-11-28)
Field: Materials Science — Materials Chemistry

The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated. Nanocarriers ; Computer science ; Artificial intelligence ; Machine learning ; Nanomedicine ; Drug delivery ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Nanoparticle-Based Drug Delivery ; Radiomics and Machine Learning in Medical Imaging
Authors: Priyanka Kashid | H. K. Suresh | Shiddalingayya Mathad | Rakesh M. Shedam | Mahadev R. Shedam
Source: International Journal of Advanced Science and Engineering (review, 2022-08-01)
Field: Materials Science — Materials Chemistry

A Review on Synthesis, Properties and Applications on Cobalt Ferrite Cobalt ferrite has attracted much attention owing to its excellent properties and huge technological applications. The novel synthesis methods such as co-precipitation method, solid state method, sol-gel method, auto combustion method, precursor technique, hydrothermal technique, spray pyrolysis technique etc. have been used for its preparation. Along with numerous outstanding properties of cobalt ferrite several chemical modifications like modifying reaction conditions, substituting one or more ions with other metal ions enhanced the configuration of grains of cobalt ferrite. With all the factors the role of dopant in chemical composition is extreme important because it modifies the structural, electrical, magnetic and catalytic properties of cobalt ferrite. In this review, we summarize the synthesis and properties of cobalt ferrites with significant developments. The applications of cobalt ferrite in several fields are also highlighted in this review. Cobalt ; Ferrite (magnet) ; Materials science ; Dopant ; Chemical engineering ; Nanotechnology ; Metallurgy ; Doping ; Composite material ; Optoelectronics ; Engineering Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications
Authors: Ahmad I. Ayesh
Source: Physics Letters A (article, 2022-04-26)
Field: Materials Science — Materials Chemistry

The effect of doping MoSe2 by clusters of noble metals on its adsorption for NH3 The development of industrial activities is associated with negative impacts on the quality of the environment including emission of hazardous gases. Therefore, it is critical to produce sensitive and selective gas detectors in order to monitor and maintain its good quality. This work investigates the effect of noble metal doping of MoSe2 on its adsorption of ammonia (NH3) gas. Pristine and noble metal doped MoSe2 structures are examined computationally using density functional theory (DFT). Clusters of timers of each Pt, Au, and Ag along with their combination are utilized for doping. The influence of doping MoSe2 structure on its adsorption energy and distance of NH3 gas, density of states (DOS), charge transfer between NH3 and the MoSe2 structures, and band structure is explored. When compared to the pristine structure, the doped MoSe2 structures reveal significant changes in electronic characteristics that are reflected on the DOS and band structures. New energy bands have evolved near to the Fermi level because of modifying the MoSe2 structure. The adsorption capacity of the doped structures is greatly enhanced due to incorporation of noble metals. Herein, the magnitude of adsorption energy increases and the adsorption distance deceases for NH3 gas. Consequently, the sensitivity is improved in comparison to the pristine structure. This study shows that noble metal doping of MoSe2 by clusters of trimers can be a useful approach for developing sensitive NH3 gas detectors. Doping ; Adsorption ; Fermi level ; Noble metal ; Electronic band structure ; Materials science ; Chemical physics ; Density of states ; Density functional theory ; Fermi energy ; Metal ; Condensed matter physics ; Physical chemistry ; Physics ; Chemistry ; Optoelectronics ; Computational chemistry ; Metallurgy ; Nuclear physics 2D Materials and Applications ; MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Khushboo Singh | Foez Ahmed | Karu P. Esselle
Source: Crystals (article, 2022-12-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic Metasurfaces: Insight into Evolution, Design and Applications Metasurfaces have emerged as game-changing technology ranging from microwaves to optics. This article provides a roadmap to the evolution of electromagnetic metasurfaces with a focus on their synthesis techniques, materials used for their design and their recent and futuristic applications. A broad classification is provided, and the design principle is elaborated. The efficient and economical use of available computational resources is imperative to work with state-of-the-art metasurface systems. Hence, optimization becomes an integral part of metasurface design. Several optimization methodologies reported to date have been discussed. An extensive study on the current research database gathered a comprehensive understanding of meta-atom topologies and the preferred fabrication technologies. The study concludes with a critical analysis and highlights existing and future research challenges to be addressed. Computer science ; Focus (optics) ; Systems engineering ; Ranging ; Engineering ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Songyuan Li | Pengxian Song | Zhanpeng Wei | Xu Li | Qinghua Tang | Zhengzheng Meng | Ji Li | Songtao Liu | Yuhuai Wang | Jin Li
Source: Energies (article, 2022-12-29)
Field: Materials Science — Materials Chemistry

Partial Discharge Detection and Defect Location Method in GIS Cable Terminal The complex structure of gas-insulated switchgear (GIS) cable terminals leads to serious electric field concentration, which is a frequent fault position of a high-voltage cable system. At present, due to the differences in the frequency bands of sensors, various partial discharge detection technologies have certain differences in their scope of application and anti-interference performance, resulting in a low defect detection rate in GIS cable terminals. In this paper, a comprehensive diagnosis scheme is proposed, which integrates transient earth voltage (TEV), ultra-high frequency (UHF), high frequency (HF), and ultrasonic methods. Two abnormal discharge defects of GIS terminals in two 220 kV substations in Tianjin were tracked and monitored, and the joint diagnosis was carried out using the proposed scheme; the type of discharge defect and the phase sequence of the defect were determined, and the UHV was employed to precisely locate and analyze the defect source. Finally, through the disassembly analysis and electric field simulation of the GIS cable terminal, the accuracy and effectiveness of the discharge detection and location method were verified, providing a typical detection demonstration for the defect diagnosis of a GIS cable terminal. Partial discharge ; Switchgear ; Ultra high frequency ; Terminal (telecommunication) ; Transient (computer programming) ; Voltage ; Fault (geology) ; Interference (communication) ; Engineering ; Electrical engineering ; Computer science ; Electronic engineering ; Acoustics ; Telecommunications ; Geology High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Systems Fault Detection
Authors: Matteo Krüger | Jake Wilson | Marco Wietzoreck | Benjamin A. Musa Bandowe | Gerhard Lammel | Bertil Schmidt | Ulrich Pöschl | Thomas Berkemeier
Source: Natural Sciences (article, 2022-08-18)
Field: Chemistry — Organic Chemistry

Convolutional neural network prediction of molecular properties for aerosol chemistry and health effects Abstract Quinones are chemical compounds commonly found in air particulate matter (PM). Their redox activity can generate reactive oxygen species (ROS) and contribute to the oxidative potential (OP) of PM leading to adverse health effects of aerosols. The quinones' OP and ability to form ROS are linked to their reduction potential (RP, measured in volts), a metric for the tendency to lose electrons in redox reactions. Here, we use convolutional neural networks (CNN) as quantitative structure‐activity relationship (QSAR) models to relate the one‐electron RP of quinones to their molecular structure. For CNN training and testing, a data set of more than 100,000 quinones with associated RP values derived from density functional theory calculations was encoded in simplified molecular input line entry system (SMILES). The best performing CNN model achieved a root mean square error (RMSE) of 0.115 V for an independent test data set and outperformed linear regression models fitted on common molecular descriptors (≥ 0.140 V RMSE). Augmentation methods were newly adapted or applied to support CNN training with smaller data sets, improving RMSE by up to approximately 37% for a data set of 321 molecules. Adjusted for solvent effects, the CNN‐derived RP predictions showed good agreement with experimental data. Using the newly developed method, we identified a subset of atmospherically relevant quinones that are likely to have a high OP and play a role in aerosol health effects, which remains to be further elucidated by experimental studies. We suggest to use the presented machine learning approach in further investigations of atmospheric aerosol chemistry and health effects as well as other studies that require a target‐oriented screening of the properties and effects of large classes of substances. Key Points Convolutional neural networks can be used to estimate unknown physical and chemical properties of chemical substances and outperform additive group contribution methods. Augmentation methods aid in the prevalent problem of data availability. Quinone species detected in the environment are screened for potential relevance in atmospheric chemistry and public health and presented in this study. Mean squared error ; Test set ; Convolutional neural network ; Chemistry ; Quantitative structure–activity relationship ; Linear regression ; Biological system ; Data set ; Aerosol ; Molecular descriptor ; Redox ; Artificial neural network ; Metric (unit) ; Computational chemistry ; Artificial intelligence ; Mathematics ; Computer science ; Statistics ; Stereochemistry ; Organic chemistry ; Biology Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Yinghan Zhao | Nikolas Schiffmann | Arnd Koeppe | Nico Brandt | Ethel C. Bucharsky | Karl G. Schell | Michael Selzer | Britta Nestler
Source: Frontiers in Materials (article, 2022-02-03)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate Lithium-ion batteries with solid electrolytes offer safety, higher energy density and higher long-term performance, which are promising alternatives to conventional liquid electrolyte batteries. Lithium aluminum titanium phosphate (LATP) is one potential solid electrolyte candidate due to its high Li-ion conductivity. To evaluate its performance, influences of the experimental factors on the materials design need to be investigated systematically. In this work, a materials design strategy based on machine learning (ML) is employed to design experimental conditions for the synthesis of LATP. In the variation of parameters, we focus on the tolerance against the possible deviations in the concentration of the precursors, as well as the influence of sintering temperature and holding time. Specifically, models built with different design selection strategies are compared based on the training data assembled from previous laboratory experiments. The best one is then chosen to design new experiment parameters, followed by measuring the corresponding properties of the newly synthesized samples. A previously unknown sample with ionic conductivity of 1.09 × 10 −3 S cm −1 is discovered within several iterations. In order to further understand the mechanisms governing the high ionic conductivity of these samples, the resulting phase compositions and crystal structures are studied with X-ray diffraction, while the microstructures of sintered pellets are investigated by scanning electron microscopy. Our studies demonstrate the advantages of applying machine learning in designing experimental conditions by the synthesis of desired materials, which can effectively help researchers to reduce the number of required experiments. Materials science ; Electrolyte ; Ionic conductivity ; Lithium (medication) ; Conductivity ; Microstructure ; Sintering ; Chemical engineering ; Nanotechnology ; Composite material ; Chemistry ; Electrode Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials