Engineering topics
Publications and source records attributed to Mannodi-Kanakkithodi, Arun.
Perovskite-based photocatalytic water splitting is an efficient and sustainable technology to produce high-purity hydrogen gas for clean energy using solar energy.
Metal halide perovskites (MHPs) have a strong potential for optoelectronic applications, especially photovoltaics. A significant advantage offered by MHPs is their bandgap tunability via chemical substitution and alloying, with lead–tin alloys producing the narrowest reported bandgaps of ~1.2 eV. Efforts to further narrow the bandgap of MHPs by alloying in other elements, such as bismuth, have largely been unsuccessful due to the introduction of defective electronic states, which severely diminish electronic quality. Here, in this study, we examine the effects of bismuth on a lead–tin alloyed MHP, motivated by the narrow bandgap of alloyed lead–tin MHPs. We find that the defect screening observed in lead–tin MHPs is not sufficient to screen the defect level introduced by bismuth, as evidenced by quenched photoluminescence, decreased mobility, and severely reduced performance in a photovoltaic device. Density functional theory calculations suggest that midgap states are introduced by bismuth addition over a range of chemical and compositional conditions. We further observe through wavelength-dependent photoconductivity that free carriers are generated out to ~0.9 eV in bismuth-containing samples, which could be of potential interest for NIR photodetection.
Copper has been used as a p-type dopant in cadmium telluride (CdTe) for decades. However, the density of Cu atoms in the finished device is much higher than that of holes, which means that most Cu atoms are not activated as acceptors during incorporation. Furthermore, studies have demonstrated that the distribution of copper (Cu) atoms across the device is highly inhomogeneous, with reports citing Cu substitution on Cd sites and segregation to grain boundaries. Fast diffusion along these boundaries and Cu accumulation at the CdTe/CdS interface have also been observed and validated computationally. These levels of inhomogeneity make it difficult to accurately characterize and correlate the performance with the nature of the Cu atomic species present. To address this challenge, we utilize X-ray microscopy and, specifically, nanoscale fluorescence-mode X-ray absorption near-edge structure to resolve the atomic Cu environment throughout the depth of the CdTe layer. Our results suggest that the majority of Cu atoms are in the form of Cu x Te phases (or similar local environments) near the ZnTe|CdTe interface, Cu x O phases in the CdTe absorber, and present in various oxidation states, including Cu 1+ and Cu 2+ , near the CdS/CdTe junction. Here this work also provides experimental evidence for the first time of the presence of CuS around the ZnTe|CdTe interface and the hypothesized Cu Cd -Cl i complex in the CdTe absorber.
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The burgeoning field of materials informatics necessitates a focus on educating the next generation of materials scientists in the concepts of data science, artificial intelligence (AI), and machine learning (ML). In addition to incorporating these topics in undergraduate and graduate curricula, regular hands-on workshops present the most effective medium to initiate researchers to informatics and have them start applying the best AI/ML tools to their own research. With the help of the Materials Research Society (MRS), members of the MRS AI Staging Committee, and a dedicated team of instructors, we successfully conducted workshops covering the essential concepts of AI/ML as applied to materials data, at both the Spring and Fall Meetings in 2022, with plans to make this a regular feature in future meetings. Here, in this article, we discuss the importance of materials informatics education via the lens of these workshops, including details such as learning and implementing specific algorithms, the crucial nuts and bolts of ML, and using competitions to increase interest and participation.
We report x-ray absorption near edge structure (XANES) is a powerful tool to probe the fingerprint of local structures, and when coupled with X-ray microscopy, the small spot size enables one to probe very specific regions of interest in a device or material, e.g. interfaces/bulk, different grains, good/bad electrical areas. In this work, we investigate the use of linear combination fitting (LCF) of XANES spectra for the particular case of Cu doping in CdTe. We show that the experimental data seem to be accurately represented by standards of Cu 2 Te and its substoichiometric counterpart, Cu 1.43 Te. We use Cu in CdTe as a case study to evaluate the accuracy of linear combination fitting using simulated standards, given that experimental standards for certain phases (e.g. Cu 1.43 Te) or defect structures (e.g. vacancies) cannot be readily obtained. We discuss how spectral features of the FEFF9-simulated standards, fitting ranges, and noise levels all dictate the accuracy of this type of analysis. We show that the greater the spectral difference between the two standards, the better the LCF is able to differentiate between the two structures and to tolerate experimental noise. Finally, we estimate the error of the fitted weights for different spectral features and noise levels and propose a framework to study local structures semi-quantitatively by using binary mixtures of FEFF9-simulated standards.