Bridging the Gap between Quantitative and Qualitative Accelerated Life Tests

Test planners have long sought the ability to incorporate the results of highly accelerated life testing (HALT) into an early estimate of system reliability. While case studies attest to the effectiveness of HALT in producing reliable products, the capability to translate the test's limited failure data into a meaningful measure of reliability improvement remains elusive. Further, a review of quality and reliability literature indicates that confusion exists over what defines a HALT and how HALT differs from quantitative accelerated life testing methods. Despite many authors making a clear distinction between qualitative and quantitative accelerated life tests, an explanation as to why this delineation exists cannot be found. In this paper, we consider an exemplary HALT composed of a single stressor to show that the HALT philosophy precludes the estimation of a system's hazard rate function parameters because of the test's fix implementation strategy. Four common accelerated failure data analysis methods are highlighted to show their limitations with respect to estimating reliability from HALT data. Finally, a modified accelerated reliability growth test is proposed as a way forward for future research in HALT scenarios to characterize the risk of attaining a reliability requirement and improve parameter estimation. Copyright © 2014 John Wiley & Sons, Ltd.     

Tribological Characteristics of Aqueous Graphene Oxide,Graphitic Carbon Nitride,and Their Mixed Suspensions

The tribological performance of graphene oxide (GO), graphitic carbon nitride (g-C₃N₄), and their mixed (g-C₃N₄/GO) aqueous suspensions was investigated. The 0.06 wt% GO, 0.06 wt% g-C₃N₄, and 0.06 wt% 1:1 g-C₃N₄/GO suspensions reduced the coefficient of friction (COF) by 37, 26 and 37% and wear mark radius by 19.1, 16.0 and 19.6%, respectively, in comparison with water. Pure g-C₃N₄ and GO suspensions showed unstable lubrication in the tests with relatively high loads and speeds, while the g-C₃N₄/GO mixed suspension had superior tribological performance in all tested conditions. This is because in the mixed suspension g-C₃N₄ agglomerates became smaller, and GO nanosheets exhibited fewer wrinkles and less stacking, which enabled the formation of a layer of tribo-composite film. As a result, the friction, wear and tribo-corrosion were reduced during sliding.     

Optimization of CdTe thin‐film solar cell efficiency using a sputtered,oxygenated CdS window layer

A major source of loss in cadmium sulfide/cadmium telluride (CdS/CdTe) solar cells results from light absorbed in the CdS window layer, which is not converted to electrical current. This film can be made more transparent by oxygen incorporation during sputter deposition at ambient temperature. Prior to this work, this material has not produced high‐efficiency devices on tin oxide‐coated soda‐lime‐glass substrates used industrially. Numerous devices were fabricated over a variety of process conditions to produce an optimized device. Although the material does not show a consistent increase in band gap with oxygenation, absorption in this layer can be virtually eliminated over the relevant spectrum, leading to an increase in short‐circuit current. Meanwhile, fill factor is maintained, and open‐circuit voltage increases relative to baseline devices with sublimated CdS. The trend of device parameters with oxygenation and thickness is consistent with an increasing conduction band offset at the window/CdTe interface. Optimization considering both initial efficiency and stability resulted in a National Renewable Energy Laboratory verified 15.2%‐efficient cell on 3.2‐mm soda‐lime glass. This window material was shown to be compatible with SnO₂‐based transparent conducting oxide and high resistance transparent coated substrates using in‐line compatible processes. Copyright © 2015 John Wiley & Sons, Ltd     

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SARS-CoV-2 Spike Protein Binding of Glycated Serum Albumin—Its Potential Role in the Pathogenesis of the COVID-19 Clinical Syndromes and Bias towards Individuals with Pre-Diabetes/Type 2 Diabetes and Metabolic Diseases

The immune response to SARS-CoV-2 infection requires antibody recognition of the spike protein. In a study designed to examine the molecular features of anti-spike and anti-nucleocapsid antibodies, patient plasma proteins binding to pre-fusion stabilised complete spike and nucleocapsid proteins were isolated and analysed by matrix-assisted laser desorption ionisation–time of flight (MALDI-ToF) mass spectrometry. Amongst the immunoglobulins, a high affinity for human serum albumin was evident in the anti-spike preparations. Careful mass comparison revealed the preferential capture of advanced glycation end product (AGE) forms of glycated human serum albumin by the pre-fusion spike protein. The ability of bacteria and viruses to surround themselves with serum proteins is a recognised immune evasion and pathogenic process. The preference of SARS-CoV-2 for AGE forms of glycated serum albumin may in part explain the severity and pathology of acute respiratory distress and the bias towards the elderly and those with (pre)diabetic and atherosclerotic/metabolic disease.     

Simulating the Feasibility of Using Liquid Micro-Jets for Determining Electron–Liquid Scattering Cross-Sections

The extraction of electron–liquid phase cross-sections (surface and bulk) is proposed through the measurement of (differential) energy loss spectra for electrons scattered from a liquid micro-jet. The signature physical elements of the scattering processes on the energy loss spectra are highlighted using a Monte Carlo simulation technique, originally developed for simulating electron transport in liquids. Machine learning techniques are applied to the simulated electron energy loss spectra, to invert the data and extract the cross-sections. The extraction of the elastic cross-section for neon was determined within 9% accuracy over the energy range 1–100 eV. The extension toward the simultaneous determination of elastic and ionisation cross-sections resulted in a decrease in accuracy, now to within 18% accuracy for elastic scattering and 1% for ionisation. Additional methods are explored to enhance the accuracy of the simultaneous extraction of liquid phase cross-sections.     

Optimized excitation energy transfer in a three-dye luminescent solar concentrator

The spectral range of sunlight absorbed by a luminescent solar concentrator (LSC) is increased by using multiple dyes. Absorption, fluorescence, and fluorescence excitation spectra, and relative light output are reported for LSCs made with one, two, or three BODIPY dyes in a thin polymer layer on glass. Losses caused by multiple emission and reabsorption events are minimized by optimizing resonance excitation energy transfer between dyes. Increases in the outputs from the multiple-dye LSCs are directly proportional to increases in the number of photons absorbed. The output of the three-dye LSC is 45–170% higher than those of the single-dye LSCs.     

Estimating percent impervious cover from Landsat-based land cover with a simple and transferable regression model

Percent impervious cover (PIC) is a widely used metric in ecological and hydrological analyses because it is highly correlated with pollutant and storm water run-off. The moderate-resolution satellite data (e.g. Landsat), that are typically used to calculate PIC, tend to overestimate PIC for all but very rural and very urban landscapes. Existing models for calibrating PIC estimates (e.g. ISAT, ETIS) are limited in that they are applicable only for specific land cover datasets and may also require population data; furthermore, these models have not been tested for performance outside of the geographic locations in which they were developed. The goal of this study was to explore simple but widely applicable regression models as tools for calibrating PIC estimates based on moderate resolution satellite data. The regression models used impervious land cover, from Landsat-based datasets, as the sole predictor of actual PIC. PIC was calculated for analysis units, ranging in size from 2.25 ha to ≥100 ha, for locations in Connecticut, Massachusetts, and Ohio in the United States. Regression models were fit for each size class of analysis unit at each study location; generalized versions of the models were created by fitting a regression to all size classes of analysis units at a given study location. Calibrated PIC estimates had root mean square error (RMSE) values that ranged from 1.5–10.7%; these values were considerably better than RMSE values for uncalibrated PIC estimates which ranged from 10.1–23.3%. For both calibrated and uncalibrated PIC, the accuracy of the estimates improved with the increasing size of the analysis units. Model regression coefficients were similar regardless of the analysis unit size, geographic location, or land cover dataset; model performance declined only slightly when applied outside the area in which it was developed. The simple regression models developed in this study had similar performance to previous calibration models (i.e. ISAT, ETIS) but are easier to apply and more widely applicable.