Computational Smocking through Fabric-Thread Interaction

We formalize Italian smocking, an intricate embroidery technique that gathers flat fabric into pleats along meandering lines of stitches, resulting in pleats that fold and gather where the stitching veers. In contrast to English smocking, characterized by colorful stitches decorating uniformly shaped pleats, and Canadian smocking, which uses localized knots to form voluminous pleats, Italian smocking permits the fabric to move freely along the stitched threads following curved paths, resulting in complex and unpredictable pleats with highly diverse, irregular structures, achieved simply by pulling on the threads. We introduce a novel method for digital previewing of Italian smocking results, given the thread stitching path as input. Our method uses a coarse-grained mass-spring system to simulate the interaction between the threads and the fabric. This configuration guides the fine-level fabric deformation through an adaptation of the state-of-the-art simulator, C-IPC. Our method models the general problem of fabric-thread interaction and can be readily adapted to preview Canadian smocking as well. We compare our results to baseline approaches and physical fabrications to demonstrate the accuracy of our method

Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model

Chemical reactions are the fundamental building blocks of drug design and organic chemistry research. In recent years, there has been a growing need for a large-scale deep-learning framework that can efficiently capture the basic rules of chemical reactions. In this paper, we have proposed a unified framework that addresses both the reaction representation learning and molecule generation tasks, which allows for a more holistic approach. Inspired by the organic chemistry mechanism, we develop a novel pretraining framework that enables us to incorporate inductive biases into the model. Our framework achieves state-of-the-art results on challenging downstream tasks. By possessing chemical knowledge, our generative framework overcome the limitations of current molecule generation models that rely on a small number of reaction templates. In the extensive experiments, our model generates synthesizable drug-like structures of high quality. Overall, our work presents a significant step toward a large-scale deep-learning framework for a variety of reaction-based applications

Data_Sheet_1_Association between oral microbiome and five types of respiratory infections: a two-sample Mendelian randomization study in east Asian population.zip

ObjectiveTo explore the causal relationship between the oral microbiome and specific respiratory infections including tonsillitis, chronic sinusitis, bronchiectasis, bronchitis, and pneumonia, assessing the impact of genetic variations associated with the oral microbiome.MethodsMendelian randomization was used to analyze genetic variations, leveraging data from genome-wide association studies in an East Asian cohort to identify connections between specific oral microbiota and respiratory infections.ResultsOur analysis revealed that Prevotella, Streptococcus, Fusobacterium, Pauljensenia, and Capnocytophaga play crucial roles in influencing respiratory infections. Prevotella is associated with both promoting bronchitis and inhibiting pneumonia and tonsillitis, with a mixed effect on chronic sinusitis. Streptococcus and Fusobacterium show varied impacts on respiratory diseases, with Fusobacterium promoting chronic sinusitis, bronchiectasis, and bronchitis. Conversely, Pauljensenia and Capnocytophaga are linked to reduced bronchitis and tonsillitis, and inhibited pneumonia and bronchitis, respectively.DiscussionThese findings underscore the significant impact of the oral microbiome on respiratory health, suggesting potential strategies for disease prevention and management through microbiome targeting. The study highlights the complexity of microbial influences on respiratory infections and the importance of further research to elucidate these relationships.</p

Table_1_Association between oral microbiome and five types of respiratory infections: a two-sample Mendelian randomization study in east Asian population.XLSX

ObjectiveTo explore the causal relationship between the oral microbiome and specific respiratory infections including tonsillitis, chronic sinusitis, bronchiectasis, bronchitis, and pneumonia, assessing the impact of genetic variations associated with the oral microbiome.MethodsMendelian randomization was used to analyze genetic variations, leveraging data from genome-wide association studies in an East Asian cohort to identify connections between specific oral microbiota and respiratory infections.ResultsOur analysis revealed that Prevotella, Streptococcus, Fusobacterium, Pauljensenia, and Capnocytophaga play crucial roles in influencing respiratory infections. Prevotella is associated with both promoting bronchitis and inhibiting pneumonia and tonsillitis, with a mixed effect on chronic sinusitis. Streptococcus and Fusobacterium show varied impacts on respiratory diseases, with Fusobacterium promoting chronic sinusitis, bronchiectasis, and bronchitis. Conversely, Pauljensenia and Capnocytophaga are linked to reduced bronchitis and tonsillitis, and inhibited pneumonia and bronchitis, respectively.DiscussionThese findings underscore the significant impact of the oral microbiome on respiratory health, suggesting potential strategies for disease prevention and management through microbiome targeting. The study highlights the complexity of microbial influences on respiratory infections and the importance of further research to elucidate these relationships.</p

Tandem Solar Cells Using GaAs Nanowires on Si: Design, Fabrication, and Observation of Voltage Addition

Multijunction solar cells provide us a viable approach to achieve efficiencies higher than the Shockley–Queisser limit. Due to their unique optical, electrical, and crystallographic features, semiconductor nanowires are good candidates to achieve monolithic integration of solar cell materials that are not lattice-matched. Here, we report the first realization of nanowire-on-Si tandem cells with the observation of voltage addition of the GaAs nanowire top cell and the Si bottom cell with an open circuit voltage of 0.956 V and an efficiency of 11.4%. Our simulation showed that the current-matching condition plays an important role in the overall efficiency. Furthermore, we characterized GaAs nanowire arrays grown on lattice-mismatched Si substrates and estimated the carrier density using photoluminescence. A low-resistance connecting junction was obtained using n⁺-GaAs/p⁺-Si heterojunction. Finally, we demonstrated tandem solar cells based on top GaAs nanowire array solar cells grown on bottom planar Si solar cells. The reported nanowire-on-Si tandem cell opens up great opportunities for high-efficiency, low-cost multijunction solar cells

Toward Optimized Light Utilization in Nanowire Arrays Using Scalable Nanosphere Lithography and Selected Area Growth

Vertically aligned, catalyst-free semiconducting nanowires hold great potential for photovoltaic applications, in which achieving scalable synthesis and optimized optical absorption simultaneously is critical. Here, we report combining nanosphere lithography (NSL) and selected area metal–organic chemical vapor deposition (SA-MOCVD) for the first time for scalable synthesis of vertically aligned gallium arsenide nanowire arrays, and surprisingly, we show that such nanowire arrays with patterning defects due to NSL can be as good as highly ordered nanowire arrays in terms of optical absorption and reflection. Wafer-scale patterning for nanowire synthesis was done using a polystyrene nanosphere template as a mask. Nanowires grown from substrates patterned by NSL show similar structural features to those patterned using electron beam lithography (EBL). Reflection of photons from the NSL-patterned nanowire array was used as a measure of the effect of defects present in the structure. Experimentally, we show that GaAs nanowires as short as 130 nm show reflection of <10% over the visible range of the solar spectrum. Our results indicate that a highly ordered nanowire structure is not necessary: despite the “defects” present in NSL-patterned nanowire arrays, their optical performance is similar to “defect-free” structures patterned by more costly, time-consuming EBL methods. Our scalable approach for synthesis of vertical semiconducting nanowires can have application in high-throughput and low-cost optoelectronic devices, including solar cells

GaAs Nanowire Array Solar Cells with Axial p–i–n Junctions

Because of unique structural, optical, and electrical properties, solar cells based on semiconductor nanowires are a rapidly evolving scientific enterprise. Various approaches employing III–V nanowires have emerged, among which GaAs, especially, is under intense research and development. Most reported GaAs nanowire solar cells form p–n junctions in the radial direction; however, nanowires using axial junction may enable the attainment of high open circuit voltage (<i>V</i>_oc) and integration into multijunction solar cells. Here, we report GaAs nanowire solar cells with axial p–i–n junctions that achieve 7.58% efficiency. Simulations show that axial junctions are more tolerant to doping variation than radial junctions and lead to higher <i>V</i>_oc under certain conditions. We further study the effect of wire diameter and junction depth using electrical characterization and cathodoluminescence. The results show that large diameter and shallow junctions are essential for a high extraction efficiency. Our approach opens up great opportunity for future low-cost, high-efficiency photovoltaics

Electrical and Optical Characterization of Surface Passivation in GaAs Nanowires

We report a systematic study of carrier dynamics in Al_<i>x</i>Ga_1–<i>x</i>As-passivated GaAs nanowires. With passivation, the minority carrier diffusion length (<i>L</i>_diff) increases from 30 to 180 nm, as measured by electron beam induced current (EBIC) mapping, and the photoluminescence (PL) lifetime increases from sub-60 ps to 1.3 ns. A 48-fold enhancement in the continuous-wave PL intensity is observed on the same individual nanowire with and without the Al_<i>x</i>Ga_1–<i>x</i>As passivation layer, indicating a significant reduction in surface recombination. These results indicate that, in passivated nanowires, the minority carrier lifetime is not limited by twin stacking faults. From the PL lifetime and minority carrier diffusion length, we estimate the surface recombination velocity (SRV) to range from 1.7 × 10³ to 1.1 × 10⁴ cm·s^–1, and the minority carrier mobility μ is estimated to lie in the range from 10.3 to 67.5 cm² V^–1 s^–1 for the passivated nanowires