A biofunctional polymeric coating for microcantilever molecular recognition

An innovative route to activate silicon microcantilevers (MCs) for label free molecular recognition is presented. The method consists in coating the underivatized MCs with a functional ter-polymer based on N,N-dimethylacrylamide (DMA) bearing N-acryloyloxysuccinimide (NAS) and 3-(trimethoxysilyl)propyl-methacrylate (MAPS), two functional monomers that confer to the polymer the ability to react with nucleophilic species on biomolecules and with glass silanols, respectively. The polymer was deposited onto MCs by dip coating. Polymer coated MCs were tested in both static and dynamic modes of actuation, featuring detection of DNA hybridization as well as protein/protein interaction. In the dynamic experiments, focused on protein detection, the MCs showed an average mass responsivity of 0.4 Hz/pg for the first resonant mode and of 2.5 Hz/pg for the second resonant mode. The results of the static experiments, dedicated to DNA hybridization detection, allowed for direct estimation of the DNA duplex formation energetics, which resulted fully consistent with the nominal expected values. These results, together with easiness and cheapness, high versatility, and excellent stability of the recognition signal, make the presented route a reliable alternative to standard SAM functionalization (for microcantilevers (MCs) and for micro-electro-mechanical systems (MEMS) in general).     

GaN的补偿度与离子束沟道最小产额比的关系的研究

用ＲＢＳ／沟道技术对ＭＯＣＶＤ生长的未故意掺杂的ＧａＮ的结构性能进行了测试，同时用霍耳方法测试了样品的电学性能。结果表明：ＧａＮ薄膜的背散射沟道说与随机谱之比Ｘｍｉｎ和其补偿度存在一定的依赖关系。补偿度小的样品，其Ｘｍｉｎ小；随着样品补偿度的增大，Ｘｍｉｎ也逐渐增大；但它们之间的知系变化是非线性的。对这些结果给予了一定的解释。     

Measurement of excitation spectra for highly ionized sulphur

The excitation spectra in the wavelength range of 19—40 nm for highly ionized sulphur ions were measured by using the beam-foil method at the Heavy lon Research Facility in Lanzhou. In this experiment, more than 30 spectral lines were observed, which belonged to the transitions of the excitation energy levels for highly ionized SX—SXV ions, and 5 new lines were determined. The experimental results were compared with those from other experiments and theoretical calculations.     

Chemical Kinetics Database and Predictive Schemes for Humid Air Plasma Chemistry. Part I: Positive Ion–Molecule Reactions

Reliable kinetic and thermodynamic data are required to model the evolution of electric discharge or electron-beam decomposition chemistry of gases in humid air streams. In this first segment of a continuing series, we provide a core database describing the initially dominant ion-neutral molecule reactions in humid air plasmas. Recommended reaction rate data and extrapolation tools are presented in a manner to facilitate prediction of reactivities and reaction channels as a function of temperature, pressure, and applied electric field.     

Variational Quantum Chemistry Programs in JaqalPaq

We present example quantum chemistry programs written with JaqalPaq, a python meta-programming language used to code in Jaqal (Just Another Quantum Assembly Language). These JaqalPaq algorithms are intended to be run on the Quantum Scientific Computing Open User Testbed (QSCOUT) platform at Sandia National Laboratories. Our exemplars use the variational quantum eigensolver (VQE) quantum algorithm to compute the ground state energies of the H2, HeH+, and LiH molecules. Since the exemplars focus on how to program in JaqalPaq, the calculations of the second-quantized Hamiltonians are performed with the PySCF python package, and the mappings of the fermions to qubits are obtained from the OpenFermion python package. Using the emulator functionality of JaqalPaq, we emulate how these exemplars would be executed on an error-free QSCOUT platform and compare the emulated computation of the bond-dissociation curves for these molecules with their exact forms within the relevant basis.     

Artificial solid-electrolyte interphase (SEI) for improved cycleability and safety of lithium–ion cells for EV applications

Since the SEI is one of the most vulnerable factors in the safety of the lithium–ion battery, improvement in the stability of the SEI will result in a safer battery with better performance characteristics. In this work an artificial SEI was formed on graphite and tin–copper anodes by electropainting and vacuum-insertion techniques. The artificial SEI was found to stabilize the structure of the Sn–Cu anode and led to a cycle life for the cell that was longer by a factor of five and irreversible capacity less than half that of the pristine anode.     

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique,Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.     

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.