Vacancy island creation and coalescence using automated scanning tunneling microscopy

We demonstrate that scanning tunneling microscope tip-surface crash events can be utilized as an efficient means for the creation of predefined island configurations for diffusion studies. Using this method, islands of varying size can be created and placed in close proximity, increasing the probability of initiating and observing coalescence events. Data obtained from crash initiated events on a Ag(111) surface are presented. Relaxation time exponents extracted from these data confirm that our method gives results consistent with previous, sputter-obtained island coalescence studies. We also describe an instrument-control routine developed for these measurements that utilizes commercial imaging and off-the-shelf automation software to automate the tracking of islands or other features by the microscope.     

Wedged multilayer Laue lens

A multilayer Laue lens (MLL) is an x-ray focusing optic fabricated from a multilayer structure consisting of thousands of layers of two different materials produced by thin-film deposition. The sequence of layer thicknesses is controlled to satisfy the Fresnel zone plate law and the multilayer is sectioned to form the optic. An improved MLL geometry can be created by growing each layer with an in-plane thickness gradient to form a wedge, so that every interface makes the correct angle with the incident beam for symmetric Bragg diffraction. The ultimate hard x-ray focusing performance of a wedged MLL has been predicted to be significantly better than that of a nonwedged MLL, giving subnanometer resolution with high efficiency. Here, we describe a method to deposit the multilayer structure needed for an ideal wedged MLL and report our initial deposition results to produce these structures.     

美国火电厂烟气排放限值及控制技术

美国火电厂烟气排放限值及控制技术在过去的37年内不断发展进步.受限制的排放物越来越多,而且对每种污染物的限值日益严格.总结了当今美国使用的排放限值及控制技术,并展望了未来的排放限值及潜在的排放控制技术.     

Characterization of commercial double-walled carbon nanotube material: composition, structure, and heat capacity

A purified commercial double-walled carbon nanotube (DWCNT) sample was investigated by transmission electron microscopy (TEM), thermogravimetry (TG), and Raman spectroscopy. Moreover, the heat capacity of the DWCNT sample was determined by temperature-modulated differential scanning calorimetry in the range of temperature between −50 and 290 °C. The main thermo-oxidation characterized by TG occurred at 474 °C with the loss of 90 wt% of the sample. Thermo-oxidation of the sample was also investigated by high-resolution TG, which indicated that a fraction rich in carbon nanotube represents more than 80 wt% of the material. Other carbonaceous fractions rich in amorphous coating and graphitic particles were identified by the deconvolution procedure applied to the derivative of TG curve. Complementary structural data were provided by TEM and Raman studies. The information obtained allows the optimization of composites based on this nanomaterial with reliable characteristics.     

Evolution and stability of texture during thermomechanical processing of Ti-24Al-11Nb alloy

The evolution of basal texture during thermomechanical processing of Ti-24Al-11Nb alloy has been studied as a function of different processing variables like hot rolling temperature, amount of deformation, cooling conditions etc. The stability of the deformation texture during post-rolling annealing and during theα ₂→β→α ₂ phase transformation cycle was also investigated. Unrestricted rolling of primaryα ₂ to maximum thickness reduction at the lowest rolling temperature has been found to be most favourable for obtaining a good basal texture. Texture of transformed (secondary)α ₂ is generally non basal when the transformation takes place from deformedβ. Rolling texture does not seem to change during annealing leading to recrystallization. Theα ₂→β→α ₂ phase transformation cycle does not change the starting basal texture and a starting non basal texture also does not give rise to basal texture due to this treatment.     

Managing cooperation in e-business systems

E-Business management involves monitoring and controlling all forms of business transactions conducted over the Internet, extranets, and related communications technologies and services. As such, it includes the management of networks, systems, and software applications. E-Business management is beginning to depend increasingly on knowledge of the cooperative aspects of partners in a business chain and the cooperative aspects of stake-holders in e-business operations. The study of cooperation offers many opportunities to apply social network analyses to derive software solutions for the management of e-business. This article provides a methodology and case study that recognises the dimension of cooperation among a multiplicity of organisations, coined “Awareness-based Cooperative Management.” It is aimed towards the design or re-engineering of effective e-business management systems, where the key concept involves the degree of awareness held by e-business stake-holders. Awareness is defined in terms of stake-holder interactions and artefacts used in those interactions such as buyer specifications, requests for proposals, offers, service level agreements, and the like. Since awareness is an indispensable, albeit an inherently vague concept, we cast it and reason about it with the apparatus of fuzzy logic.

Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers

Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in order to develop a mortality prediction model for hospitalised SARS-CoV-2 positive patients. A retrospective cohort study including data from suspected SARS-CoV-2 positive patients admitted to a large National Health Service (NHS) Foundation Trust hospital in the Yorkshire and Humber regions, United Kingdom, between 1 March 2020 and 30 August 2020. Hospitalised adult patients (aged ≥ 18 years) with at least one confirmed positive RT-PCR test for SARS-CoV-2 and blood tests of kidney biomarkers within 36 h of the RT-PCR test were included. The main outcome measure was 90-day in-hospital mortality in SARS-CoV-2 infected patients. The logistic regression and random forest (RF) models incorporated six predictors including three routine kidney function tests (sodium, urea; creatinine only in RF), along with age, sex, and ethnicity. The mortality prediction performance of the logistic regression model achieved an area under receiver operating characteristic (AUROC) curve of 0.772 in the test dataset (95% CI: 0.694–0.823), while the RF model attained the AUROC of 0.820 in the same test cohort (95% CI: 0.740–0.870). The resulting validated prediction model is the first to focus on kidney biomarkers specifically on in-hospital mortality over a 90-day period.     

Moisture Sensitive Smart Yarns and Textiles from Self‐Balanced Silk Fiber Muscles

Smart textiles that sense, interact, and adapt to environmental stimuli have provided exciting new opportunities for a variety of applications. However, current advances have largely remained at the research stage due to the high cost, complexity of manufacturing, and uncomfortableness of environment‐sensitive materials. In contrast, natural textile materials are more attractive for smart textiles due to their merits in terms of low cost and comfortability. Here, water fog and humidity‐driven torsional and tensile actuation of thermally set twisted, coiled, plied silk fibers, and weave textiles from these silk fibers are reported. When exposed to water fog, the torsional silk fiber provides a fully reversible torsional stroke of 547° mm^?1. Coiled‐and‐thermoset silk yarns provide a 70% contraction when the relative humidity is changed from 20% to 80%. Such an excellent actuation behavior originates from water absorption‐induced loss of hydrogen bonds within the silk proteins and the associated structural transformation, which are corroborated by atomistic and macroscopic characterization of silk and molecular dynamics simulations. With its large abundance, cost‐effectiveness, and comfortability for wearing, the silk muscles will open up additional possibilities in industrial applications, such as smart textiles and soft robotics.     

Determination of IgG1 and IgG3 SARS-CoV-2 Spike Protein and Nucleocapsid Binding—Who Is Binding Who and Why?

The involvement of immunoglobulin (Ig) G3 in the humoral immune response to SARS-CoV-2 infection has been implicated in the pathogenesis of acute respiratory distress syndrome (ARDS) in COVID-19. The exact molecular mechanism is unknown, but it is thought to involve this IgG subtype’s differential ability to fix, complement and stimulate cytokine release. We examined the binding of convalescent patient antibodies to immobilized nucleocapsids and spike proteins by matrix-assisted laser desorption/ionization–time of flight (MALDI-ToF) mass spectrometry. IgG3 was a major immunoglobulin found in all samples. Differential analysis of the spectral signatures found for the nucleocapsid versus the spike protein demonstrated that the predominant humoral immune response to the nucleocapsid was IgG3, whilst for the spike protein it was IgG1. However, the spike protein displayed a strong affinity for IgG3 itself, as it would bind from control plasma samples, as well as from those previously infected with SARS-CoV-2, similar to the way protein G binds IgG1. Furthermore, detailed spectral analysis indicated that a mass shift consistent with hyper-glycosylation or glycation was a characteristic of the IgG3 captured by the spike protein.     

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system. This work has been supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant No. W911NF-07-1-0376, by the U.S. Office of Naval Research under Grant No. N00014-08-1-380, and by NASA under Cooperative Agreement No. NNX07AK49A.