Combined Theoretical and Experimental Investigation and Design of H2S Tolerant Anode for Solid Oxide Fuel Cells

A solid oxide fuel cell (SOFC) is a high temperature fuel cell and it normally operates in the range of 850 to 1000 C. Coal syngas has been considered for use in SOFC systems to produce electric power, due to its high temperature and high hydrogen and carbon monoxide content. However, coal syngas also has contaminants like carbon dioxide (CO{sub 2}) and hydrogen sulfide (H{sub 2}S). Among these contaminants, H{sub 2}S is detrimental to electrode material in SOFC. Commonly used anode material in SOFC system is nickel-yttria stabilized zirconia (Ni-YSZ). The presence of H{sub 2}S in the hydrogen stream will damage the Ni anode and hinder the performance of SOFC. In the present study, an attempt was made to understand the mechanism of anode (Ni-YSZ) deterioration by H{sub 2}S. The study used computation methods such as quantum chemistry calculations and molecular dynamics to predict the model for anode destruction by H{sub 2}S. This was done using binding energies to predict the thermodynamics and Raman spectroscopy to predict molecular vibrations and surface interactions. On the experimental side, a test stand has been built with the ability to analyze button cells at high temperature under syngas conditions

A straightforward route to sensor selection for IoT systems

The Internet of Things (IoT) allows for remote management and monitoring of many aspects of everyday life at the individual and industrial levels. However, designing these systems within constraints of cost and operational context can be a real challenge. The sensor network must be strategically designed, which means selecting the most appropriate sensors to collect a specific measurement in a specific environment and then optimizing their deployment and utilization. To facilitate sensor selection, we propose a straightforward, color-coded, three-sieve selection tool and demonstrate the efficacy of this method through real-life exemplars. The selection tool could be applied to other kinds of technologies as well

Perovskite solar cell for photocatalytic water splitting with a TiO2/Co-doped hematite electron transport bilayer

© 2018 The Royal Society of Chemistry. Hydrogen production using a photoelectrochemical (PEC) route promises to be a clean and efficient way of storing solar energy for use in hydrogen-powered fuel cells. Iron oxide (α-Fe2O3) is best suited to be used as a photoelectrode in PEC cells for solar hydrogen production due to its favorable band gap of ∼2.2 eV. Herein, chemical solution deposition was used for the preparation of a series of Co-doped Fe2O3 thin films on a titania buffer layer at different doping concentrations (3.0, 7.0 and 10.0 at%). The maximum anodic photocurrent reached up to 3.04 mA cm-2 by optimizing the balance between the doping concentrations, enhanced donor density, light absorbance, and surface roughness. The optical properties show that the light absorbance tendency switches to the higher wavelength with the further increment of Co beyond 3.0%. Finally, synthesized photosensitive perovskite CH3NH3PbI3 materials were added as a surface treatment agent on the photoelectrode to enhance the photocurrent absolute value. This inorganic nanostructured perovskite CH3NH3PbI3 (MAPbI3) coated on the Co-doped hematite photoanode achieved an overall solar-to-hydrogen conversion efficiency of 2.46%. Due to its low temperature processing, stability, and enhance efficiency, this perovskite coated TiO2/Co-doped hematite multilayer thin film solar cell has high potential to be applied in industry for hydrogen production

Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning

COVID-19 has been in the headlines for the past two years. Diagnosing this infection with minimal false rates is still an issue even with the advent of multiple rapid antigen tests. Enormous data are being collected every day that could provide insight into reducing the false diagnosis. Machine learning (ML) and deep learning (DL) could be the way forward to process these data and reduce the false diagnosis rates. In this study, ML and DL approaches have been applied to the data set collected using an ultra-fast COVID-19 diagnostic sensor (UFC-19). The ability of ML and DL to specifically detect SARS-CoV-2 signals against SARS-CoV, MERS-CoV, Human CoV, and Influenza was investigated. UFC-19 is an electrochemical sensor that was used to test these virus samples and the obtained current response dataset was used to diagnose SARS-CoV-2 using different algorithms. Our results indicate that the convolution neural networks algorithm could diagnose SARS-CoV-2 samples with a sensitivity of 96.15%, specificity of 98.17%, and accuracy of 97.20%. Combining this DL model with the existing UFC-19 could selectively identify SARS-CoV-2 presence within two minutes

Ultra-Fast Electrochemical Sensor for Point-of-Care COVID-19 Diagnosis Using Non-Invasive Saliva Sampling

Point-of-care diagnostic devices that are rapid and reliable remain as an unmet need highlighted by the coronavirus disease (COVID-19) pandemic crisis. The second/third wave of virus spread in various parts of the world combined with new evidence of re-infections and inadequate healthcare facilities demand increased testing rate to diagnose COVID-19 at its core. Although traditional molecular diagnostic tests have served this purpose, there have been shortage of reagents and other supplies at pandemic frontlines. This calls for novel alternate diagnostic processes with potential for obtaining emergency use authorization and that can be deployed in the field at the earliest opportunity. Here, we show an ultra-fast SARS-CoV-2 detection sensor for detecting coronavirus proteins in saliva within 100 milliseconds. Electrochemical oxidation of nickel hydroxide has been controlled using cyclic voltammetry and chronoamperometry techniques for successful detection of SARS-CoV-2. Test results have proven the capability of sensors to quantitatively detect the concentration of virus in blinded analyses. The detection occurs by a process similar to that of SARS-CoV-2 binding onto host cells. The sensor also shows prospects in distinguishing SARS-CoV-2 from other viruses such as HIV. More importantly, the sensor matches the detection limit of the gold standard test for diagnosing early infection. The use of saliva as a non-invasive sampling technique combined with the portability of the instrument has broadened the potential of this sensor

Effect of Ammonia on Pt, Ru, Rh, and Ni Cathodes During the Alkaline Hydrogen Evolution Reaction

The effects of ammonia on the hydrogen evolution reaction (HER) with Pt, Rh, Ru, and Ni have been investigated using electrochemical methods. The activity of the catalysts for HER, in the presence of ammonia follows the trend Rh > Pt > Ru > Ni. Pt and Ru exhibit a decrease in the activity toward HER. Cyclic voltammetric (CV) studies show that the presence of ammonia results in weaker metal–hydrogen underpotential deposition (M–H_upd) bonds. In addition, the activity of HER on Pt and Ru is retarded in the presence of ammonia, supporting the theory that the activity for HER is higher on the metal surface with H_upd than on the bare metal. On the contrary, the activity for HER on Rh surface in the presence of ammonia is enhanced, suggesting the M–H_upd bond is a barrier to the HER. CV for Ni in the presence of ammonia shows a Ni­(OH)₂ reduction peak. However, the overpotentials for HER on Ni are very high such that no significant difference is observed in the activity of HER

SARS-CoV-2 Surveillance in Indoor Air Using Electrochemical Sensor for Continuous Monitoring and Real-Time Alerts

The severe acute respiratory syndrome related coronavirus 2 (SARS-CoV-2) has spread globally and there is still a lack of rapid detection techniques for SARS-CoV-2 surveillance in indoor air. In this work, two test rigs were developed that enable continuous air monitoring for the detection of SARS-CoV-2 by sample collection and testing. The collected samples from simulated SARS-CoV-2 contaminated air were analyzed using an ultra-fast COVID-19 diagnostic sensor (UFC-19). The test rigs utilized two air sampling methods: cyclone-based collection and internal impaction. The former achieved a limit of detection (LoD) of 0.004 cp/L in the air (which translates to 0.5 cp/mL when tested in aqueous solution), lower than the latter with a limit of 0.029 cp/L in the air. The LoD of 0.5 cp/mL using the UFC-19 sensor in aqueous solution is significantly lower than the best-in-class assays (100 cp/mL) and FDA EUA RT-PCR test (6250 cp/mL). In addition, the developed test rig provides an ultra-fast method to detect airborne SARS-CoV-2. The required time to test 250 L air is less than 5 min. While most of the time is consumed by the air collection process, the sensing is completed in less than 2 s using the UFC-19 sensor. This method is much faster than both the rapid antigen (<20 min) and RT-PCR test (<90 min)