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Tunability in electronic and optical properties of GaS/PbS vdW heterostructure
No full textA promising novel class of heterostructures has recently emerged, combining a semiconducting GaS monolayer with other 2D materials for energy-related applications. In this study, we considered the layered PbS to form the van der Waals heterostructure with GaS and investigated its properties using density functional theory. The GaS/PbS heterostructure exhibits a type-II heterostructure with an indirect bandgap of 1.65 eV, displaying enhanced light absorption across the visible spectrum. Moreover, the heterostructure\u27s energy band gap shows tunability with an applied transverse electric field attributed to the spontaneous electric polarization within the lattice. Subsequently, it contributes to increased optical absorbance and light harvesting efficiency under ±0.2 V/Å electric field. The applied electric field also offers tunable band alignments (transition type-II and type-I), making it a potential candidate for solar cells that can optimize their efficiency based on varying light conditions
First-principles study of thermoelectric properties of the bulk and Sb-doped orthorhombic ZnAs and CdAs
No full textZnAs and CdAs, synthesized in the orthorhombic face, are the focus of this study to establish their electronic and thermoelectric properties. Results based on density functional theory find both materials exhibiting dynamical and mechanical stability, along with low lattice thermal conductivity. Doping with Sb in the arsenide lattice leads to higher charge carrier concentration, which, in turn, enhances its thermoelectric performance significantly. Overall, the results provide insights into potential strategies for improving ZT value by tailoring the charge carrier concentration in the bulk arsenides by doping
Long oligos: direct chemical synthesis of genes with up to 1728 nucleotides
Get PDFThe longest oligos that can be chemically synthesized are considered to be 200-mers. Here, we report direct synthesis of an 800-mer green fluorescent protein gene and a 1728-mer Φ29 DNA polymerase gene on an automated synthesizer. Key innovations that enabled this breakthrough include conducting the synthesis on a smooth surface rather than within the pores of traditional supports, and the use of the powerful catching-by-polymerization (CBP) method for isolating the full-length oligos from a complex mixture. Conducting synthesis on a smooth surface not only eliminated the steric hindrance that would otherwise prevent long oligo assembly, but also, surprisingly, drastically reduced synthesis errors. Compared with the benchmark PCR assembly gene synthesis method, the direct long oligo synthesis method has the advantages of higher probability to succeed, fewer sequence restrictions, and being able to synthesize long oligos containing difficult elements such as unusually stable higher-order structures, long repeats, and site-specific modifications. The method is expected to open doors for various projects in areas such as synthetic biology, gene editing, and protein engineering
Inspiring the next generation of engineers and scientists to be champions of equitable change
No full textNyasha Milanzi is a winner of the fifth annual Rising Black Scientists Awards for a scholar in the physical, data, earth, and environmental sciences. We asked emerging Black scientists to tell us about their scientific vision and goals, experiences that sparked their interest in science, how they want to contribute to a more inclusive scientific community, and how these all fit together on their journey. This is her story
A novel computational method to predict hypoattenuated leaflet thickening post-transcatheter aortic valve replacement using preprocedural computed tomography scans
No full textObjective: Hypoattenuated leaflet thickening (HALT) is a computed tomography (CT) finding after transcatheter aortic valve replacement (TAVR) that is indicative of bioprosthetic valvular thrombosis. There are currently no standardized or validated methods for predicting HALT, which can cause bioprosthetic valve dysfunction and has been associated with adverse patient outcomes. The objective was to develop a novel fast-response, artificial intelligence, and machine learning (ML)- driven computational pipeline to predict HALT using preprocedural CT scans. Methods: The pipeline consisted of (1) pre-TAVR CT reconstruction and reduced order modeling simulations to automatically predict postprocedural geometric parameters, (2) a landmark-guided automated left ventricle segmentation method to predict hemodynamic parameters, and (3) statistical and ML analyses to develop HALT predictive metrics. Results: Pre- and postprocedural scans from 45 patients (21 with HALT, 24 without) were used as inputs for the pipeline. We identified statistically significant relationships between HALT and peak systolic blood velocity (P\u3c.01) and peak systolic blood flow through the bioprosthetic valve (P \u3c .01), left ventricular ejection time (P \u3c .01), ejection volume (P \u3c .05), and right coronary height (P \u3c .05). ML-yielded metrics related to circulation in the neosinuses correlated strongly with HALT occurrence (P \u3c .001) along with the greatest accuracy of 84.40% and area under receiver operating characteristic curve of 0.87. Conclusions: A computational pipeline using pre-procedural CT scans as inputs that outputs post-TAVR geometric and hemodynamic measurements was developed to assess metrics with the potential to predict the risk of HALT. Such a tool may help guide decision-making and understanding of prevention of postprocedural thrombosis. (JTCVS Structural and Endovascular 2025;5:100041
Deep-Red Cyanine-Based Fluorescent Probes with 6-Quinolinium Acceptors for Mitochondrial NAD(P)H Imaging in Live Cells and Human Diseased Kidney Tissues
No full textWe developed two deep-red cyanine chromophores, probes A and B, for selective mitochondrial NAD(P)H detection in live cells. Probe A features a 1,2,3,3-tetramethyl-3H-indolium core, while probe B incorporates a 1,1,2,3-tetramethyl-1H-benzo[e]indol-3-ium moiety, both linked to quinolinium via a vinyl bond to enable fluorescence modulation upon NAD(P)H reduction of probes A and B. To explore the role of electron-withdrawing groups in probe sensitivity, we synthesized three additional cyanine dyes (probes C, D, and E) via condensation of 6-quinolinecarboxaldehyde with 2,3-dimethyl-1,3-benzothiazolium acceptor and malononitrile derivatives, followed by methylation. Under NAD(P)H-deficient conditions, probe A showed absorption at 382 nm with weak fluorescence at 636 nm, while probe B absorbed at 443 nm with weak fluorescence at 618 nm. Upon NAD(P)H reduction, probe A exhibited red-shifted absorption at 520 nm with enhanced emission at 589 nm, and probe B at 550 nm with strong emission at 610 nm. Probe C showed absorption at 524 nm with enhanced emission at 586 nm, while probes D and E exhibited no detectable NAD(P)H response, highlighting the critical role of quinolinium acceptors. Probe B demonstrated superior sensitivity, successfully tracking NAD(P)H fluctuations in HeLa cells under glycolysis stimulation (glucose, lactate, pyruvate) and treatments with LPS and methotrexate. It also visualized NAD(P)H in Drosophila larvae, revealing increased levels after drug treatments. Notably, probe B distinguished between healthy and diseased human kidney tissues, detecting significantly elevated NADH levels in autosomal dominant polycystic kidney disease (ADPKD) samples, emphasizing its diagnostic potential. This study introduces probe B as a versatile and reliable NAD(P)H sensor for metabolic research and disease diagnostics, offering valuable insights into redox processes in live cells, organisms, and clinical samples
Polarization statistics of thermal microwave radiation
No full textTime series of thermal emissions from water vapor and air molecules at ≈2.8GHz, collected by the National Center for Atmospheric Research\u27s (NCAR) S-band polarimetric radar S-Pol are examined for polarization probability distributions. A nearly uniform distribution of states on the Poincaré sphere surface is found. This uniformity is consistent with the four-dimensional circular Gaussian distribution of electric fields (pairs of in-phase and quadrature components for two orthogonal directions) for unpolarized radiation. Analysis of experimental data and the derived sampling distribution of the degree of polarization of the finite time series both yield a mean of about 0.02, within bounds of sampling variability of unpolarized Gaussian-distributed radiation. Weak inhomogeneity of polarization states on the Poincaré sphere detected in the experimental data is within the error bounds of the radar receiver
ThermalTrack Dataset- Training Images- Fused RGB LWIR- sequence 3
Get PDFWe present a wheel track detection system that leverages RGB- Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow- tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable
HYBRID MIXTURES OF FACTOR ANALYZERS FOR HIGH DIMENSIONAL DATA
No full textFactor analysis is a powerful tool for modeling latent structures in high-dimensional data, traditional approaches assume a single global structure, limiting their ability to capture heterogeneity. The Mixture of Factor Analyzers (MFA) extends classical factor analysis by modeling data as a mixture of Gaussian-distributed local subspaces, effectively uncovering cluster-specific latent structures. However, MFA relies on Gaussian mixtures, making it sensitive to outliers and ill-suited for heavy-tailed data. The Mixture of -Factor Analyzers (MFA) addresses these limitations by incorporating multivariate -distributions, improving robustness. Despite their advantages, both MFA and MFA face significant computational challenges in high-dimensional settings, particularly due to costly covariance matrix operations and slow convergence of the Expectation-Maximization (EM) algorithm. In this collection of work, we propose a hybrid approach that integrates a matrix-free algorithm within the EM framework to improve computational efficiency for both MFA and MFA. Our methods preserve the full covariance structure while leveraging matrix-free strategies to optimize computations in high dimensions. This approach maintains the interpretability and flexibility of mixture-based factor models while making them more scalable. Empirical evaluations on synthetic and real-world data reveals that our method significantly improves speed, robustness, while preserving clustering accuracy compared to conventional MFA and MFA models driven by standard EM algorithms. These findings highlight the effectiveness of matrix-free strategies in advancing mixture-based latent variable models for high-dimensional data analysis
Kangaroo: Dynamic Fusion of Branch Instructions in a Pipelined Uniprocessor
Get PDFSmall pipelined processors are becoming more common as a complement to superscalars in a multi-core chip. However, current uniprocessors offer little in the way of ILP. We present kangaroo, a novel approach to instruction fusion in a pipelined processor. Kangaroo dynamically fuses two adjacent instructions to create a pair that travels through the pipeline as a unit. The instructions re-enter the pipeline as a pair the next time the first instruction is fetched. Unlike in prior art, an instruction, once fused, is not fetched again. Any pair of adjacent instructions can be fused using this technique, including dependent instructions. In this thesis, we focus on fusing branches to prior instructions. We introduce the fusion mechanism and discuss its implementation. Finally, we evaluate its performance with respect to a baseline six-stage pipelined uniprocessor with several benchmarks