Detection of ZnS phases in CZTS thin-films by EXAFS

Copper zinc tin sulfide (CZTS) is a promising Earthabundant thin-film solar cell material; it has an appropriate band gap of ~1.45 eV and a high absorption coefficient. The most efficient CZTS cells tend to be slightly Zn-rich and Cu-poor. However, growing Zn-rich CZTS films can sometimes result in phase decomposition of CZTS into ZnS and Cu2SnS3, which is generally deleterious to solar cell performance. Cubic ZnS is difficult to detect by XRD, due to a similar diffraction pattern. We hypothesize that synchrotron-based extended X-ray absorption fine structure (EXAFS), which is sensitive to local chemical environment, may be able to determine the quantity of ZnS phase in CZTS films by detecting differences in the second-nearest neighbor shell of the Zn atoms. Films of varying stoichiometries, from Zn-rich to Cu-rich (Zn-poor) were examined using the EXAFS technique. Differences in the spectra as a function of Cu/Zn ratio are detected. Linear combination analysis suggests increasing ZnS signal as the CZTS films become more Zn-rich. We demonstrate that the sensitive technique of EXAFS could be used to quantify the amount of ZnS present and provide a guide to crystal growth of highly phase pure films

Test of a Homeopathic Algorithm for COVID-19: the Importance of a Broad Perspective

Abstract Background/Objective Most of the symptoms of coronavirus disease 2019 (COVID-19) are covered by large repertory rubrics and hence many remedies have been proposed as “genus epidemicus”. The aim of this study was to combine the information from various data collections to prepare a COVID-19 Bayesian mini-repertory/an algorithm-based application (app) and test it. Methods In July 2021, 1,161 COVID-19 cases from 100 practitioners globally were combined. These data were used to calculate “condition-confined” likelihood ratios (LRs) for 59 symptoms of COVID-19. Out of these, 35 symptoms of the 11 medicines that had at least 20 cases each were considered. The information was entered in a spreadsheet (algorithm) to calculate combined LRs of specific combinations of symptoms. The algorithm contained the medicines Arsenicum album, Belladonna, Bryonia alba, Camphora, Gelsemium sempervirens, Hepar sulphuris, Mercurius solubilis, Nux vomica, Phosphorus, Pulsatilla and Rhus toxicodendron. To test concordance, the doctors were then invited to re-enter the symptoms of their cases into this algorithm. Results The algorithm was re-tested on 358 cases, and concordance was seen in 288 cases. On analysis of the data, bias was noticed in the Merc group, which was therefore excluded from the algorithm. The remaining 10 medicines, representing 81.8% of all cases, were included in the preparation of the next version of the homeopathic mini-repertory and app. Conclusion The Bayesian mini-repertory and app is based on qualitative clinical experiences of various doctors in COVID-19 and gives indications for specific medicines for common COVID-19 symptoms