The integration of nanotextured black silicon (B-Si) into solar cells is often complicated by its enhanced phosphorus doping effect, which is typically attributed to increased surface area. In this article, we show that B-Si's surface-to-volume ratio, or specific surface area (SSA), which is directly related to surface reactivity, is a better indicator of reduced sheet resistance. We investigate six B-Si conditions with varying dimensions based on two morphology types prepared using metal-catalyzed chemical etching and reactive-ion etching. We demonstrate that for a POCl 3 diffusion, B-Si sheet resistance decreases with increasing SSA, regardless of surface area. 2-D dopant contrast imaging of different textures with similar surface areas also indicates that the extent of doping is enhanced with increasing SSA. 3-D diffusion simulations of nanocones show that both the extent of radial doping within a texture feature and the metallurgical junction depth in the underlying substrate increase with increasing SSA. We suggest SSA should be considered more readily when studying B-Si and its integration into solar cells. Index Terms-Black silicon, phosphorus doping, silicon nanotexture, surface area, surface-to-volume ratio. I. INTRODUCTIONN ANOTEXTURED silicon often falls under the collective label of black silicon (B-Si) which typically describes surfaces with low reflectance across a wide wavelength range (ultraviolet to near-infrared) and which appear black to the eye. B-Si can be prepared using a myriad of techniques and covers a wide range of surface morphologies and feature dimensions (from nano to micron-scale), including nanoporous layers [1],

The peptide quantitative structure−activity relationship (QSAR), also known as the quantitative sequence−activity model (QSAM), has attracted much attention in the bio-and chemoinformatics communities and is a well developed computational peptidology strategy to statistically correlate the sequence/structure and activity/property relationships of functional peptides. Amino acid descriptors (AADs) are one of the most widely used methods to characterize peptide structures by decomposing the peptide into its residue building blocks and sequentially parametrizing each building block with a vector of amino acid principal properties. Considering that various AADs have been proposed over the past decades and new AADs are still emerging today, we herein query the following: is it necessary to develop so many AADs and do we need to continuously develop more new AADs? In this study, we exhaustively collect 80 published AADs and comprehensively evaluate their modeling performance (including fitting ability, internal stability, and predictive power) on 8 QSAR-oriented peptide sample sets (QPSs) by employing 2 sophisticated machine learning methods (MLMs), totally building and systematically comparing 1280 (80 AADs × 8 QPSs × 2 MLMs) peptide QSAR models. The following is revealed: (i) None of the AADs can work best on all or most peptide sets; an AAD usually performs well for some peptides but badly for others. (ii) Modeling performance is primarily determined by the peptide samples and then the MLMs used, while AADs have only a moderate influence on the performance. (iii) There is no essential difference between the modeling performances of different AAD types (physiochemical, topological, 3D-structural, etc.). (iv) Two random descriptors, which are separately generated randomly in standard normal distribution N(0, 1) and uniform distribution U(−1, +1), do not perform significantly worse than these carefully developed AADs. (v) A secondary descriptor, which carries major information involved in the 80 (primary) AADs, does not perform significantly better than these AADs. Overall, we conclude that since there are various AADs available to date and they already cover numerous amino acid properties, further development of new AADs is not an essential choice to improve peptide QSAR modeling; the traditional AAD methodology is believed to have almost reached the theoretical limit nowadays. In addition, the AADs are more likely to be a vector symbol but not informative data; they are utilized to mark and distinguish the 20 amino acids but do not really bring much original property information to these amino acids.

Nanostructured black silicon (b-Si) surfaces with an extremely low reflectance are a promising light-trapping solution for silicon solar cells. However, it is challenging to develop a high-efficiency front-junction b-Si solar cell due to the inferior electrical performance of b-Si emitters, which outweighs any optical gain. This article uses three-dimensional numerical nanoscale simulations, which are corroborated with experiment results, to investigate the effect of the surface nanofeature sizes on the b-Si emitter performance in terms of the sheet resistance (R sheet ) and the saturation current density (J 0e). We show that the specific surface area (SSA) is an effective parameter to evaluate the nanofeature size. A shallow surface nanofeature with a large SSA will contribute to a better electrical performance. We will show that b-Si emitter R sheet measured by a four-point probe is not a measure of the doping level in the nanofeature, but is ruled by the doping level in the underlying substrate region. We also show that a small nanofeature with SSA > 100 µm -1 and height < 100 nm can lead to a relatively low J 0e (33 fA/cm 2 lower than the best b-Si results reported in the literature) by suppressing surface minority carrier density and minimizing the total Auger recombination loss.

The protein–protein association in cellular signaling networks (CSNs) often acts as weak, transient, and reversible domain–peptide interaction (DPI), in which a flexible peptide segment on the surface of one protein is recognized and bound by a rigid peptide-recognition domain from another. Reliable modeling and accurate prediction of DPI binding affinities would help to ascertain the diverse biological events involved in CSNs and benefit our understanding of various biological implications underlying DPIs. Traditionally, peptide quantitative structure-activity relationship (pQSAR) has been widely used to model and predict the biological activity of oligopeptides, which employs amino acid descriptors (AADs) to characterize peptide structures at sequence level and then statistically correlate the resulting descriptor vector with observed activity data via regression. However, the QSAR has not yet been widely applied to treat the direct binding behavior of large-scale peptide ligands to their protein receptors. In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner? Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities, which were represented by the Boehringer light units (BLUs) derived from previous arbitrary light intensity assays following SPOT peptide synthesis. Four sophisticated MLMs (MLMs) were then utilized to perform pQSAR modeling on the set described with different AADs to systematically create a variety of linear and nonlinear predictors, and then verified by rigorous statistical test. It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues. In addition, the arbitrary BLUs used to characterize DPI affinity values were measured via an indirect approach, which may not very reliable and may involve strong noise, thus leading to a considerable bias in the modeling. The Rprd2 = 0.7 can be considered as the upper limit of external generalization ability of the pQSAR methodology working on large-scale DPI affinity data.

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and extrapolative prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, for pQSARs, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, literatures, etc. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB.