Single Crystalline α-Fe2O3 Nanosheets with Improved PEC Performance for Water Splitting.

We report the photoelectrochemical (PEC) performance of a densely grown single crystalline hematite (α-Fe2O3) nanosheet photoanode for water splitting. Unlike expensive ITO/FTO substrates, the sheets were grown on a piece of pure Fe through controlled thermal oxidation, which is a facile low cost and one-step synthesis route. The sheets grow with a widest surface parallel to basal plane (0001). Iron oxide formed on Fe consisting of layer structure α-Fe2O3-Fe3O4-Fe is elucidated from GIXRD and correlated to spectral features observed in Raman and UV-vis spectroscopy. The top α-Fe2O3 nanosheet layer serves as a photoanode, whereas the conducting Fe3O4 layer serves to transport photogenerated electrons to the counter electrode through its back contact. Time-resolved photoluminescence (TRPL) measurements revealed significantly prolonged carrier lifetime compared to that of bulk. Compared to the thin film of α-Fe2O3 grown on the FTO substrate, ∼3 times higher photocurrent density (0.33 mA cm-2 at 1.23 VRHE) was achieved in the nanosheet sample under solar simulated AM 1.5 G illumination. The sample shows a bandgap of 2.1 eV and n-type conductivity with carrier density 9.59 × 1017 cm-3. Electrochemical impedance spectroscopy (EIS) measurements reveal enhanced charge transport properties. The results suggest that nanosheets synthesized by the simple method yield far better PEC performance than the thin film on the FTO substrate. The anodic shifts of flat band potential, delayed electron-hole recombination, and growth direction parallel to the highly conducting basal plane (0001) being some of the contributing factors to the higher photocurrent observed in the NS photoanode are discussed. Characterizations carried out before and after the PEC reaction show excellent stability of the nanosheets in an alkaline electrochemical environment.

Utilization of Time Series Tools in Life-sciences and Neuroscience.

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals', other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.