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This special issue collects selected papers from participants of the 5th International Conference on Ultrafast Structural Dynamics (ICUSD 2019), which was held at KAIST, Daejeon, South Korea, on June 25-28, 2019. Ultrafast structural dynamics span a variety of research communities, who have interest in visualizing the dynamics of atomic/molecular assemblies in space (sub-Å) and time (femtosecond) at the atomic scale using a variety of experimental techniques such as ultrafast electron/x-ray diffraction, scattering, spectroscopy, time-resolved x-ray/electron microscopy for dynamic imaging of matter, ultrafast multidimensional vibrational/electronic spectroscopies, x-ray/electron serial crystallography, and ultrafast Raman/IR/optical spectroscopies. Fortunately, the nine papers covered most of the topics and fields presented in the conference although some of the important ones are missing, for example, time-resolved serial crystallography, time-resolved electron/x-ray imaging, and time-resolved electron/x-ray microscopy. [Extracted from the article]

This paper deals with the possibility of merging spider silk with nanomaterials by directly feeding them to the spiders. Indeed, creating a soft "bionicomposite" with enhanced mechanical and/or other multifunctional properties, e.g., electric, magnetic, etc., is attractive for material science. Pugno and co-workers were the first to expose spiders to carbon-based nanomaterials, reporting promising results in terms of silk maximal reinforcements. In a subsequent paper, Kelly and co-workers used a different approach and did not obtain any significant strengthening in the silk. These different results highlight the importance of exploring the issue better. In this work, spiders were exposed to nanomaterial solutions with different protocols, and the properties of their silk were monitored for 14 days, displaying a strong protocol influence and inherent day-to-day variability (up to 300% of a single property). This made this paper's results aligned with both the previous mentioned works, pinpointing the key challenge to merging silk and nanomaterials using spiders. This work should stimulate further studies and discussion on the topic. [ABSTRACT FROM AUTHOR]

In transitioning toward a sustainable economy, mycelial materials are recognized for their adaptability, biocompatibility, and eco-friendliness. This paper updates the exploration of mycelial materials, defining their scope and emphasizing the need for precise terminology. It discusses the importance of mycelial type and characteristics, reviews existing and future research directions, and highlights the need for improved understanding, clarity, and standardization in this emerging field, aiming to foster and guide future research and development in sustainable material science. [ABSTRACT FROM AUTHOR]

Atomic force microscopy is highly suited for characterizing morphology and physical properties of nanoscale objects. The application of this technique to nanomechanical studies is, therefore, exploited in a wide range of fields from life sciences to materials science and from miniature devices to sensors. Although performing a mechanical measurement can be straightforward and accessible to novice users, obtaining meaningful results requires knowledge and experience not always evident in standard instrumental software modules. In this paper, we provide a basic guide to proper protocols for the measurement and analysis of force curves and related atomic force microscopic techniques. Looking forward, we also survey the budding application of machine learning in this discipline. [ABSTRACT FROM AUTHOR]

Future materials-science research will involve autonomous synthesis and characterization, requiring an approach that combines machine learning, robotics, and big data. In this paper, we highlight our recent experiments in autonomous synthesis and resistance minimization of Nb-doped TiO2 thin films. Combining Bayesian optimization with robotics, these experiments illustrate how the required speed and volume of future big-data collection in materials science will be achieved and demonstrate the tremendous potential of this combined approach. We briefly discuss the outlook and significance of these results and advances. [ABSTRACT FROM AUTHOR]

We introduce a broadband tunable femtosecond laser source in the long-wave infrared (LWIR) band, covering the range of 5–13.5 μm, based on the integration of optical parametric amplification and difference frequency generation techniques. We utilize a dual-stage tuning method, combined with the high nonlinear coefficient and broadband phase matching range of the BaGa4Se7 crystal, to facilitate significant improvements in spectral coverage and energy efficiency. The laser yields a peak output energy of 43 μJ and maintains energies above 10 μJ across the entire tuning range, with an average power output exceeding 10 mW. The pulse duration at the central wavelength of 8.3 μm is measured at 72 fs full width at half-maximum using the electro-optic sampling method. This LWIR femtosecond laser can be used in many applications, such as molecular fingerprint spectral analysis, ultrafast chemical reaction spectral analysis, materials science, and ultrafast physics research, providing an important research basis for the generation and application of mid-infrared ultrafast laser sources. [ABSTRACT FROM AUTHOR]

The optimized multi-head graph attention network (OPNet) uses the multi-head graph attention network to predict both the thermal decomposition temperature with a 5% mass loss (Td5%) and the glass transition temperature (Tg) of polymers as a machine learning model. The OPNet model provides reliable performance predictions for the Td5% and Tg datasets. The OPNet regression evaluation metrics R2 of Td5% is 0.76, which is the best, and the regression evaluation metrics R2 of Tg is 0.91, which is better than the current existing method models (the best model R2 ≈ 0.90). The OPNet model is an end-to-end network model, eliminating the need for manual data filtering or feature extraction. By analyzing the feature weights of the OPNet model, it is found that structures such as benzene rings play a more important role. At the same time, we have confirmed through other literature that such structures do have better stability and a higher thermal decomposition temperature and glass transition temperature. Therefore, the OPNet model exhibits interpretability and holds significant reference value for the field of materials science. [ABSTRACT FROM AUTHOR]

This paper investigates hybrid function projective synchronization in complex dynamical networks. When the complex dynamical networks could be synchronized up to an equilibrium or periodic orbit, a hybrid feedback controller is designed to realize the different component of vector of node could be synchronized up to different desired scaling function in complex dynamical networks with time delay. Hybrid function projective synchronization (HFPS) in complex dynamical networks with constant delay and HFPS in complex dynamical networks with time-varying coupling delay are researched, respectively. Finally, the numerical simulations show the effectiveness of theoretical analysis. [ABSTRACT FROM AUTHOR]

Control of the oxidation process is one key issue in producing high-quality emitters for crystalline silicon solar cells. In this paper, the oxidation parameters of pre-oxidation time, oxygen concentration during pre-oxidation and pre-deposition and drive-in time were optimized by using orthogonal experiments. By analyzing experimental measurements of short-circuit current, open circuit voltage, series resistance and solar cell efficiency in solar cells with different sheet resistances which were produced by using different diffusion processes, we inferred that an emitter with a sheet resistance of approximately 70 ω/⇔ performed best under the existing standard solar cell process. Further investigations were conducted on emitters with sheet resistances of approximately 70 ω/⇔ that were obtained from different preparation processes. The results indicate that emitters with surface phosphorus concentrations between 4.96 x 1020 cm-3 and 7.78 x 1020 cm-3 and with junction depths between 0.46 μm and 0.55 μm possessed the best quality.With no extra processing, the final preparation of the crystalline silicon solar cell efficiency can reach 18.41%, which is an increase of 0.4%abs compared to conventional emitters with 50 ω/⇔ sheet resistance. [ABSTRACT FROM AUTHOR]

In this paper, a novel type of white-light organic light emitting diode (OLED) with high color stability was reported, in which the yellow-light emission layer of (4,4'-N,N'-dicarbazole)biphenyl (CBP) : tris(2-phenylquinoline-C2,N')iridium(III) (Ir(2-phq)3) was sandwiched by double blue-light emission layers of 1,1-bis-[(di- 4-tolylamino)pheny1]cyclohexane (TAPC) : bis[4,6-(di-fluorophenyl)-pyridinato- N,C2']picolinate (FIrpic) and tris[3-(3-pyridyl)mesityl]borane (3TPYMB):FIrpic. And, it exhibited the maximum current efficiency of 33.1 cd/A, the turn-on voltage at about 3 V and the maximum luminance in excess of 20000 cd/m². More important, it realized very stable white-light emission, and its CIE(x, y) coordinates only shift from (0.34, 0.37) to (0.33, 0.37) as applied voltage increased from 5 V to 12 V. It is believed that the new scheme in emission layer of white-light OLED can fine tune the contribution of primary emission with applied voltage changed, resulting in high quality white-light OLED. [ABSTRACT FROM AUTHOR]

Predicting the microstructure of composite plates based on deformation data under static loads is crucial for advanced materials design and optimization. This study utilizes finite element simulations to generate deformation data, capturing the complex mechanical behavior of composite materials under static loading conditions. We developed a deep learning model based on a multi-layer perceptron (MLP) architecture to predict the microstructure of these composite plates from the simulated deformation data. The model is trained on a dataset comprising diverse microstructural patterns and their corresponding deformation responses. Our results demonstrate the MLP's capability to accurately infer microstructural details, highlighting the potential of deep learning in materials science. This approach not only enhances the understanding of the relationship between deformation and microstructure but also provides a robust framework for designing composite materials with desired properties through computational methods. [ABSTRACT FROM AUTHOR]

Over the past several decades, there has been a significant surge in interest regarding the use of organic–inorganic hybrid polymers and nanocomposite membranes. The reasons for this are improved attributes, reduced costs, and the additional stability the influence membrane provides. This Review outlines the various techniques and methodologies used to prepare Nafion and its composites, delineating the promising benefits of the electrospinning process. Electrospinning has emerged as a versatile and promising technique for fabricating nanofibers with unique properties and wide-ranging applications. This study explores the electrospinning of Nafion, a perfluorosulfonic acid polymer widely known for its exceptional proton conductivity and chemical stability, into nanofibrous structures, unlocking new possibilities yet unknown features of its inherent properties. The morphology and chemical structure of the resulting nanofibers is analyzed. A thorough bibliographic analysis of electrospun Nafion was presented using the PRISMA approach for methodically presenting the report. Network visualization of connected authors and categorizing application-specific publications are also discussed. Moreover, the electrospinning parameters and blends are systematically investigated to optimize the production of Nafion nanofibers for various applications in fuel cells, water treatment, actuators, sensors, and energy harvesting. The challenges involved in electrospinning Nafion, Nafion nanocomposites, and their variants are also presented, with a discussion delineating the future scope. This work concludes by emphasizing the interdisciplinary character of the Nafion polymer and its composites, connecting materials science and the intricate issues presented by various sectors. [ABSTRACT FROM AUTHOR]

A list of books received for review is presented, including "Polymer Electronics" by Mark Geoghegan and Georges Hadziioannou, "Introduction to Computational Materials Science: Fundamentals to Applications" by Richard Lesar and "Molecular Aggregation: Structure Analysis and Molecular Simulation of Crystals and Liquids" by Angelo Gavezzotti.

Purity metallic materials are increasingly demanded in modern manufacturing industries, but their applications are limited owing to their poor wear resistance and mechanical properties. Therefore, exploring an efficient hardening method to significantly enhance the hardness of pure metals is emergent in materials science. In this work, a series of high pressure and high temperature (HPHT) quenching experiments were carried out on several pure metals, with a maximum hardening factor exceeding 10. The results indicated that pressure has an unusual effect on refining grains and increasing the Hall–Petch coefficient ky. The ky value of pure Fe is 49.5 GPa*μm1/2 with a quenching pressure of 5 GPa, which is two orders higher than that of the untreated polycrystalline sample (0.2 GPa*μm1/2). In addition, we report an extreme hardness of 8.34 GPa in pure Ti induced by HPHT quenching, and the unprecedented hardening comes from the formation of the twin and lath martensitic substructures. The hardening mechanism of the HPHT quenching method is a combination of Hall–Petch hardening and work-hardening. Our results provide a practical route to achieve attractive mechanical properties in pure metals and shine a light on the hardening mechanism of metallic materials. [ABSTRACT FROM AUTHOR]

Although germanium (Ge) is a semiconductor frequently used in many facets of materials science, its optical applications are limited because of an indirect band structure, which significantly diminishes absorption and emission efficiency. However, sufficiently high levels of tin (Sn) alloying enable an indirect-to-direct band structure crossover, resulting in improved optical properties. Moreover, the bandgap of GeSn alloys can be tuned by simply varying the alloy composition; therefore, the material can be modified for compatibility with silicon (Si) based electronics. While lattice mismatch makes the solubility of Sn in Ge extremely low in bulk alloys (<1%), metastable nanoalloys produced under nonequilibrium conditions show minimum to no lattice strain, allowing the synthesis of GeSn nanoalloys with wider tunability of Sn (up to 95%). Furthermore, the size-tunable confinement energy characteristic of GeSn nanoalloys has been shown to greatly increase the energy gaps, resulting in tunable visible to near-IR optical properties. Herein, the authors summarize recent advances in the synthesis of 0D and 1D GeSn alloy nanostructures and their emerging physical properties in light of their potential applications in advanced electronic and photonic technologies. [ABSTRACT FROM AUTHOR]

The ability to resolve the dynamics of matter on its native temporal and spatial scales constitutes a key challenge and convergent theme across chemistry, biology, and materials science. The last couple of decades have witnessed ultrafast electron diffraction (UED) emerge as one of the forefront techniques with the sensitivity to resolve atomic motions. Increasingly sophisticated UED instruments are being developed that are aimed at increasing the beam brightness in order to observe structural signatures, but so far they have been limited to low average current beams. Here, we present the technical design and capabilities of the HiRES (High Repetition-rate Electron Scattering) instrument, which blends relativistic electrons and high repetition rates to achieve orders of magnitude improvement in average beam current compared to the existing state-of-the-art instruments. The setup utilizes a novel electron source to deliver femtosecond duration electron pulses at up to MHz repetition rates for UED experiments. Instrument response function of sub-500 fs is demonstrated with < 100 fs time resolution targeted in future. We provide example cases of diffraction measurements on solid-state and gas-phase samples, including both micro- and nanodiffraction (featuring 100 nm beam size) modes, which showcase the potential of the instrument for novel UED experiments. [ABSTRACT FROM AUTHOR]

This paper presents an infrared transparent frequency selective surface (ITFSS) based on metallic meshes. In this ITFSS structure, periodic cross-slot units are integrated on square metallic meshes empowered by coating and UV-lithography. A matching condition is proposed to avoid the distortion of units. Experimental results show that this ITFSS possesses a good transmittance of 80% in the infrared band of 3-5 μm, and also a stable band-pass behavior at the resonance frequency of 36.4 GHz with transmittance of -0.56 dB. Theoretical simulations about the ITFSS diffractive characteristics and frequency responses are also investigated. The novel ITFSS will attract renewed interest and be exploited for applications in various fields. [ABSTRACT FROM AUTHOR]

This paper presents an efficient closed-form solution (ECS) for acoustic emission(AE) source location in three-dimensional structures using time difference of arrival (TDOA) measurements from N receivers, N ≥ 6. The nonlinear location equations of TDOAare simplified to linear equations. The unique analytical solution ofAE sources for unknown velocity system is obtained by solving the linear equations. The proposed ECS method successfully solved the problems of location errors resulting from measured deviations of velocity as well as the existence and multiplicity of solutions induced by calculations of square roots in existed close-form methods. [ABSTRACT FROM AUTHOR]

Research on room temperature terahertz (THz) detector is essential for promoting the application of THz science and technology. Both lithium tantalate crystal (LiTaO3) and lithium tantalate thin film were used to fabricate the THz detector in this paper. Polishing process were used to reduce the thickness of LiTaO3 crystal slice by chemical mechanical polishing techniques and an improved sol-gel process was used to obtain high concentration LiTaO3 precursor solution to fabricate LiTaO3 thin film. Three dimension models of two THz detectors were set up and the temperature increasing map of two devices were simulated using finite element method. The lowest noise equivalent power value for terahertz detector using pyroelectric material reaches 6.8 x 10-9 W at 30 Hz operating frequency, which is suitable for THz imaging application. [ABSTRACT FROM AUTHOR]

The vertical structuring of GaN layers for power application purposes is a key step for successful device operation. Thus, the dry etching of GaN becomes a crucial step. While etch rates and surface roughness have been analyzed well, the sidewall angle of the etched GaN has drawn less attention. In this paper, the authors report on the influence of mask material and etch parameters in an inductively coupled plasma reactive ion etching process on the angle of the etched GaN sidewall. Deep etches up to 3.3 μm are shown. The authors show how the sidewall angle can either be adjusted to high values up to 80° or, if necessary, to small angles down to 46°. [ABSTRACT FROM AUTHOR]

Introductory textbooks in solid state physics present solvable models for illustrating the occurrence of allowed bands and forbidden gaps in the energy spectrum of Bloch electrons. However, the quantum mechanical description of electrons in non-periodic solids, such as amorphous materials, is beyond the scope of introductory courses because of its intrinsic complexity. The tight-binding approximation can account for such a scenario by letting the atomic levels vary at random from lattice site to site. We theoretically tackle the study of the average properties of the energy spectrum by introducing a transfer matrix method that allows us to obtain closed expressions for the so-called coherent potential. The coherent potential is energy-dependent and constant in space. It replaces the actual atomic random potential, thus generating a periodic effective medium with the same average properties as the non-periodic solid. We demonstrate that the average density of states can be calculated within this framework without relying on heavy mathematical machinery. Thus, our approach is suitable for introductory courses in solid state physics and materials science. [ABSTRACT FROM AUTHOR]

Transition metal oxide materials are of great utility, with a diversity of topical applications ranging from catalysis to electronic devices. Because of their widespread importance in materials science, there is increasing interest in developing computational tools capable of reliable prediction of transition metal oxide phase behavior and properties. The workhorse of materials theory is density functional theory (DFT). Accordingly, we have investigated the impact of various correlation and exchange approximations on their ability to predict the properties of NiO using DFT. We have chosen NiO as a particularly challenging representative of transition metal oxides in general. In so doing, we have provided validation for the use of the r2SCAN density functional for predicting the materials properties of oxides. r2SCAN yields accurate structural properties of NiO and a local spin moment that notably persists under pressure, consistent with experiment. The outcome of our study is a pragmatic scheme for providing electronic structure data to enable the parameterization of interatomic potentials using state-of-the-art artificial intelligence (AI) and machine learning (ML) methodologies. The latter is essential to allow large scale molecular dynamics simulations of bulk and surface materials phase behavior and properties with ab initio accuracy. [ABSTRACT FROM AUTHOR]

Scientific databases offer remarkable potential for solving complex questions in materials science, such as global optimization of materials and designing unknown materials for novel properties. ThermoElectric materials eXplorer (TEXplorer) is a web-based platform designed to collect and share all types of thermoelectric materials data, including synthesis information, materials characterization, transport measurements, and electronic structures obtained from experiments and computations. TEXplorer also provides valuable tools, such as an easy upload and download system, retrieval, automatic post-processing calculations, visualization of datasets, and toolkits for predicting thermoelectric properties through machine learning models. Using the platform, we collected and managed the thermoelectric dataset of SnSe and Bi2Te3 with various doping/alloying elements in this study in order to investigate the complex relationship between doping/alloying elements and the thermoelectric properties of host materials. The web-based interactive data platform enables efficient management and utilization of experimental and computational datasets, supporting the acceleration of data-driven materials research and autonomous material synthesis. [ABSTRACT FROM AUTHOR]

In material science domain, the data availability has made it possible to design and test machine learning models not only to strengthen our understanding of various properties of materials but also to give predictive capabilities through finding trends and patterns. Here, we report the insight into magnetization of Iron based compounds using the machine learning model and by doing the model interpretability analysis using SHapley Additive exPlanations. Most of the Iron based compounds are magnetic in nature and are well studied with abundant data available in different repositories. We have used data from Materials Project. [ABSTRACT FROM AUTHOR]

Negrello I et al. i did exactly this, patterning a NiFe square ASI upon a continuous NiFe thin film.[45] The spin-wave dispersion in a Damon-Eshbach configuration is measured using Brillouin light scattering, and modes are found that are independent of the film, while others are strongly coupled with the underlayer. Specific spin-wave excitations are then selected from the dispersion relation, and the impact of the skyrmion imprint is investigated. Overall, dynamic mode coupling in an out-of-plane direction is found to modulate spin wave propagation. For nearly half a century, the era of nanoscience was driven by the paradigm that the reduction in dimensions in nanomaterials would provide a deeper understanding of the fundamental building blocks at the atomic and molecular level, resulting in novel material properties, behavior, and utilization in nanotechnologies. [Extracted from the article]

High entropy oxides are emerging as an exciting new avenue to design highly tailored functional behaviors that have no traditional counterparts. Study and application of these materials are bringing together scientists and engineers from physics, chemistry, and materials science. The diversity of each of these disciplines comes with perspectives and jargon that may be confusing to those outside of the individual fields, which can result in miscommunication of important aspects of research. In this Perspective, we provide examples of research and characterization taken from these different fields to provide a framework for classifying the differences between compositionally complex oxides, high entropy oxides, and entropy stabilized oxides, which is intended to bring a common language to this emerging area. We highlight the critical importance of understanding a material's crystallinity, composition, and mixing length scales in determining its true definition. [ABSTRACT FROM AUTHOR]

Solving electronic structure problems represents a promising field of applications for quantum computers. Currently, much effort is spent in devising and optimizing quantum algorithms for near-term quantum processors, with the aim of outperforming classical counterparts on selected problem instances using limited quantum resources. These methods are still expected to feature a runtime preventing quantum simulations of large scale and bulk systems. In this work, we propose a strategy to extend the scope of quantum computational methods to large scale simulations using a machine learning potential trained on quantum simulation data. The challenge of applying machine learning potentials in today's quantum setting arises from the several sources of noise affecting the quantum computations of electronic energies and forces. We investigate the trainability of a machine learning potential selecting various sources of noise: statistical, optimization, and hardware noise. Finally, we construct the first machine learning potential from data computed on actual IBM Quantum processors for a hydrogen molecule. This already would allow us to perform arbitrarily long and stable molecular dynamics simulations, outperforming all current quantum approaches to molecular dynamics and structure optimization. [ABSTRACT FROM AUTHOR]

In daily life, some traditional interactive modes, such as keyboards and touch pads, still have a strong vitality and practical value.[[179], [571], [573]] Based on different physical transduction mechanisms, these keyboards or touch pads are essentially tactile sensors that transduce the physical touches into electrical signals and map them to corresponding positioning units.[[572], [574]] Converting physical movement into electrical signal is the original intention of TENG, which addresses the power/energy challenges of devices with powerful sensors nowadays.[555] Therefore, the self-powered TENG technology is exactly a good solution for developing a tactile sensor equipped on these devices.[[538], [540], [574], [576], [578]] In these applications, XY matrix scheme greatly reduces the number of channels and is conducive to high resolution wiring layout.[[575], [578]] In this scheme, crosstalk and target sensing resolution are the problems. This could improve the performance of HP-based piezoelectric device for mechanical energy harvesting, design of the photoferroelectric and photopiezoelectric devices, developing hybrid energy harvesters for multi-source energy harvesting, and developing eco-friendly, flexible, and high-performance devices. Early design of such an energy-aware interface (EAI) mainly consists of a voltage detector and a switch.[[506]] Once the voltage across the supercapacitor exceeds the threshold voltage of the voltage detector, the switch will be turned on to connect the wireless sensor system to the supercapacitor. [Extracted from the article]

Characterization of grain microstructures of metallic materials is crucial to materials science and engineering applications. Unfortunately, the universal electron microscopic methodologies can only capture two-dimensional local observations of the microstructures in a time-consuming destructive way. In this regard, the nonlinear ultrasonic technique shows the potential for efficient and nondestructive microstructure characterization due to its high sensitivity to microstructural features of materials, but is hindered by the ill-posed inverse problem for multiparameter estimation induced by the incomplete understanding of the complicated nonlinear mechanical interaction mechanism. We propose an explainable nonlinearity-aware multilevel wavelet decomposition-multichannel one-dimensional convolutional neural network to hierarchically extracts multilevel time-frequency features of the acoustic nonlinearity and automatically model latent nonlinear dynamics directly from the nonlinear ultrasonic responses. The results demonstrate that the proposed approach establishes the complex mapping between acoustic nonlinearity and microstructural features, thereby determining the lognormal distribution of grain size in metallic materials rather than only average grain size. In the meantime, the integration of the designed nonlinearity-aware network and the quantitative analysis of component importance provides an acceptable physical explainability of the deep learning approach for the nonlinear ultrasonic technique. Our study shows the promise of this technique for real-time in situ evaluation of microstructural evolution in various applications. [ABSTRACT FROM AUTHOR]

Sum frequency generation vibrational spectroscopy (SFG-VS) is an intrinsically surface-selective vibrational spectroscopic technique based on the second-order nonlinear optical process. Since its birth in the 1980s, SFG-VS has been used to solve interfacial structure and dynamics in a variety of research fields including chemistry, physics, materials sciences, biological sciences, environmental sciences, etc. Better understanding of SFG-VS instrumentation is no doubt an essential step to master this sophisticated technique. To address this need, here we will present a Tutorial with respect to the classification, setup layout, construction, operation, and data processing about SFG-VS. We will focus on the steady state Ti:sapphire based broad bandwidth SFG-VS system and use it as an example. We hope this Tutorial is beneficial for newcomers to the SFG-VS field and for people who are interested in using SFG-VS technique in their research. [ABSTRACT FROM AUTHOR]

Atomic layer-by-layer molecular beam epitaxy (ALL-MBE) is a sophisticated technique to synthesize high-temperature superconductor (HTS) materials. ALL-MBE produces single-crystal HTS films with atomically smooth surfaces and interfaces, as well as precise multilayer heterostructures engineered down to a single atomic layer level. This enables the fabrication of tunnel junctions, nanowires, nanorings, and other HTS devices of interest. Our group has focused on ALL-MBE synthesis and materials science of La2−xSrxCuO4 (LSCO), a representative HTS cuprate. In the past two decades, we have synthesized over three thousand LSCO thin films and characterized them by a range of analytical techniques. Here, we present in full detail a systematic process for the synthesis and engineering of atomically perfect LSCO films. The procedure includes the preparation of substrates, calibration of the elemental sources, the recipe for ALL growth of LSCO films without any secondary-phase precipitates, post-growth annealing of the films, and ex situ film characterization. This report should aid replication and dissemination of this technique of synthesizing single-crystal LSCO films for basic research as well as for HTS electronic applications. [ABSTRACT FROM AUTHOR]

This article describes the influence of Professor David A. Shirley on the research science of one of his Ph.D. students in the diverse areas of surface magnetism, x-ray photoelectron spectroscopy (XPS), spin-resolved XPS (SRXPS), extreme ultraviolet (EUV) lithography, and hydrogen storage materials science. Examples are given from the author's work on Cr(001) surface magnetism, XPS, and SRXPS studies of multiplet-splitting in core-level photoemission from Fe. In addition, Dave's influence in understanding the radiation-induced deposition of carbon on EUV optics is described, along with the use of XPS in deciphering how hydrogen storage materials are modified by repeated hydrogen adsorption/desorption cycling. The current status of these particular topics is briefly summarized. These technical remembrances are combined with some fond personal stories about Dave, in recognition of his passing on March 29, 2021. [ABSTRACT FROM AUTHOR]

The development in materials science and pharmaceutics shows that there exist some key materials genomes, i.e., certain groups of atoms with specific constituents and structures, which govern the property of a series of materials based on them. To pinpoint such materials, genomes are helpful to assemble functional units and synthesize new materials and, thus, have a profound meaning. In this work, we develop an innovative method based on the idea of projecting atomic orbitals' wavefunction, which enables us to project every physical quantity into their orbital contribution and, thus, can be widely used to identify the materials genomes of various properties. Within this framework, we derive the expressions of the projected optical susceptibilities and exhibit a paradigm of studying the corresponding materials genomes of optical properties, especially the desired nonlinear optical materials. [ABSTRACT FROM AUTHOR]

A semi-analytical model for a double-plate capacitive proximity sensor is presented according to the effective theory. Three physical models are established to derive the final equation of the sensor. Measured data are used to determine the coefficients. The final equation is verified by usingmeasured data. The average relative error of the calculated and themeasured sensor capacitance is less than 7.5%. The equation can be used to provide guidance to engineering design of the proximity sensors. [ABSTRACT FROM AUTHOR]