Possible atomic structures for the sub-bandgap absorption of chalcogen hyperdoped silicon

Single-crystal silicon wafers were hyperdoped respectively by sulfur, selenium, and tellurium element using ion implantation and nanosecond laser melting. The hyperdoping of such chalcogen elements endowed the treated silicon with a strong and wide sub-bandgap light absorptance. When these hyperdoped silicons were thermally annealed even at low temperatures (such as 200~400 oC), however, this extra sub-bandgap absorptance began to attenuate. In order to explain this attenuation of absorptance, alternatively, we consider it corresponding to a chemical decomposition reaction from optically absorbing structure to non-absorbing structure, and obtain a very good fitting to the attenuated absorptances by using Arrhenius equation. Further, we extract the reaction activation energies from the fittings and they are 0.343(+/- 0.031) eV for S-, 0.426(+/-0.042) eV for Se-, and 0.317(+/-0.033) eV for Te-hyperdoped silicon, respectively. We discuss these activation energies in term of the bond energies of chalcogen-Si metastable bonds, and finally suggest that several high-energy interstitial sites instead of the substitutional site, are very possibly the atomic structures that are responsible for the sub-bandgap absorptance of chalcogen hyperdoped silicon.Comment: 18 pages, 3 figures, 1 tabl

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Life cycle water footprint analysis for the production of bioslurry fuels from fast pyrolysis of mallee biomass in Western Australia

It is projected that, in 2029-2030, coal will continue as a dominant fuel and take up 64% of energy market of Australia, because it supplies cheap and secure electricity generation [1]. However, combustion of coal contributes to various emissions including CO2, SO2, particulate matter (PM) and other pollutants [1]. Therefore, renewable energy, especially biomass is believed to be a vital energy source for sustainable development in the foreseeable future [2]. For example, mallee eucalypts as a key second-generation bioenergy feedstock are widely planted in the wheatbelt region of the southwest of Western Australia (WA) (300-600 mm rainfall zone) [1, 3]. However, mallee, as a kind of lignocellulosic biomass, suffers from its low volumetric energy density (about 5 GJ/m3), high moisture content (about 50%) and poor grindability, which causes the high transport cost [2]. This is unaccepted for a long-distance transport of biomass [2]. Pyrolysis as a chemical process converts biomass to a high energy product like bioslurry that can significantly reduce the transport cost [2]. However, some reports indicated the water consumption of producing bioenergy is larger than the traditional fuel such as coal [4]. Therefore, it is necessary to trace the life cycle Water Footprint (WF) of certain bioenergy production processes from the cradle to the grave. This thesis evaluates the WF of a biomass supply chain and a bioslurry supply chain in the transport and conversion stages in WA. 30 shires having abundant mallee stems resources are selected as the mallee supplying area for the Muja power station C and D units (874 MW). Also, an ideal harvesting and transport model is designed to determine the location of every farm gate of every selected shire for measuring the distances from 286 farm gates to the Muja power station, pyrolysis plant A, and pyrolysis plant B. In addition, Pyrolysis plant A (157.3 dry tonnes/day) is sited on Dalwallinu and pyrolysis plant B (203 dry tonnes/day) is sited on Wickepin, converting biomass to bioslurry, and then transport the bioslurry to the Muja power station. The result shows the annual water consumption of the bioslurry supply chain is approximately 22 times that of the biomass supply chain. However, the cost, energy, and carbon footprint of bioslurry supply chain have been proved by previous reports from Curtin University, having an advantage over the biomass supply chain in WA [2, 5]

EVA1A/TMEM166 Regulates Embryonic Neurogenesis by Autophagy

Self-renewal and differentiation of neural stem cells is essential for embryonic neurogenesis, which is associated with cell autophagy. However, the mechanism by which autophagy regulates neurogenesis remains undefined. Here, we show that Eva1a/Tmem166, an autophagy-related gene, regulates neural stem cell self-renewal and differentiation. Eva1a depletion impaired the generation of newborn neurons, both in vivo and in vitro. Conversely, overexpression of EVA1A enhanced newborn neuron generation and maturation. Moreover, Eva1a depletion activated the PIK3CA-AKT axis, leading to the activation of the mammalian target of rapamycin and the subsequent inhibition of autophagy. Furthermore, addition of methylpyruvate to the culture during neural stem cell differentiation rescued the defective embryonic neurogenesis induced by Eva1a depletion, suggesting that energy availability is a significant factor in embryonic neurogenesis. Collectively, these data demonstrated that EVA1A regulates embryonic neurogenesis by modulating autophagy. Our results have potential implications for understanding the pathogenesis of neurodevelopmental disorders caused by autophagy dysregulation

EVLncRNAs: a manually curated database for long non-coding RNAs validated by low-throughput experiments

Long non-coding RNAs (lncRNAs) play important functional roles in various biological processes. Early databases were utilized to deposit all lncRNA candidates produced by high-throughput experimental and/or computational techniques to facilitate classification, assessment and validation. As more lncRNAs are validated by low-throughput experiments, several databases were established for experimentally validated lncRNAs. However, these databases are small in scale (with a few hundreds of lncRNAs only) and specific in their focuses (plants, diseases or interactions). Thus, it is highly desirable to have a comprehensive dataset for experimentally validated lncRNAs as a central repository for all of their structures, functions and phenotypes. Here, we established EVLncRNAs by curating lncRNAs validated by low-throughput experiments (up to 1 May 2016) and integrating specific databases (lncRNAdb, LncRANDisease, Lnc2Cancer and PLNIncRBase) with additional functional and disease-specific information not covered previously. The current version of EVLncRNAs contains 1543 lncRNAs from 77 species that is 2.9 times larger than the current largest database for experimentally validated lncRNAs. Seventy-four percent lncRNA entries are partially or completely new, comparing to all existing experimentally validated databases. The established database allows users to browse, search and download as well as to submit experimentally validated lncRNAs. The database is available at here