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All-flexible chronoepifluidic nanoplasmonic patch for label-free metabolite profiling in sweat
No full textWearable sensors allow non-invasive monitoring of sweat metabolites, but their reliance on molecular recognition elements limits both physiological coverage and temporal resolution. Here we report an all-flexible chronoepifluidic surface-enhanced Raman spectroscopy (CEP-SERS) patch for label-free and chronometric profiling of sweat metabolites. The CEP-SERS patch integrates plasmonic nanostructures in epifluidic microchannels for chronological sweat sampling and molecular analysis. An ultrathin fluorocarbon nanofilm modulates surface chain mobility to guide low-temperature solid-state dewetting, forming large-area silver nanoislands on a structured flexible substrate. The wearable patch adheres conformally to skin, collects sequential sweat samples, and supports label-free and multiplexed SERS detection of assorted metabolites. Machine learning-assisted quantification of lactate, uric acid, and tyrosine yields robust metabolic profiles in distinct physical activity states. This wearable optofluidic platform refines molecular sweat sensing and expands the potential for individualized phenotyping in proactive and data-driven healthcare.
A Practical Method for Module-Level Capacitance Estimation in Automotive Power Module Using Datasheet-Based Modeling
No full textThis paper proposes a practical method for efficiently estimating the module-level capacitance of high-voltage power modules used in automotive applications. Traditionally, acquiring the complete C-V characteristics of a module required expensive high-voltage measurement equipment such as curve tracers. In contrast, this study presents a low-cost alternative that combines low-voltage measurement data with vendor datasheets to estimate high-voltage characteristics. The proposed approach begins by measuring the total capacitance of the module under low-voltage conditions (below several tens of volts) using instruments such as an LCR meter. The measured data is then compared with device-level datasheet values to extract the package-induced parasitic capacitance. This parasitic component, obtained under low-voltage conditions, is added to the high-voltage C-V data from the datasheet to estimate the total capacitance in the high-voltage region. Additionally, for the collector-emitter capacitance, which shows voltage dependence due to the parallel diodes, a nonlinear model based on PN junction theory is applied. Using the capacitances estimated by the proposed method, noise source modeling is performed and compared against models based on direct curve tracer measurements. Verification using a double pulse test shows that the proposed method achieves high accuracy in predicting high-frequency ringing and switching noise. This study demonstrates that accurate module-level capacitance estimation is possible even without high-voltage measurement equipment, relying solely on simple low-voltage measurements and datasheet information. The proposed method is applicable to various practical scenarios such as custom module design, EMI analysis, and noise source modeling.
Asymmetric scatter kernel estimation neural network for digital breast tomosynthesis
No full textPurpose: Various deep learning (DL) approaches have been developed for estimating scatter radiation in digital breast tomosynthesis (DBT). Existing DL methods generally employ an end-to-end training approach, overlooking the underlying physics of scatter formation. We propose a deep learning approach inspired by asymmetric scatter kernel superposition to estimate scatter in DBT. Approach: We use the network to generate the scatter amplitude distribution as well as the scatter kernel width and asymmetric factor map. To account for variations in local breast thickness and shape in DBT projection data, we integrated the Euclidean distance map and projection angle information into the network design for estimating the asymmetric factor. Results: Systematic experiments on numerical phantom data and physical experimental data demonstrated the outperformance of the proposed approach to UNetbased end-to-end scatter estimation and symmetric kernel-based approaches in terms of signal-to-noise ratio and structure similarity index measure of the resulting scatter corrected images. Conclusions: The proposed method is believed to have achieved significant advancement in scatter estimation of DBT projections, allowing a robust and reliable physics-informed scatter correction.
Impact of Hot Carrier Dynamics on Photoelectrocatalytic Activity on Au@Pd Antenna-Reactor Nanoparticles
No full textPhotoinduced hot carriers generated from the decay of surface plasmons in noble metals play a decisive role in producing green hydrogen gas through the photoelectrochemical (PEC) water splitting reaction, a process driven by visible light absorption. To optimize the utilization of these hot carriers, we employed a plasmonic antenna-reactor model based on core-shell structured Au@Pd nanoparticles (NPs) with an ultrathin Pd shell. In this study, we demonstrate that TiO2 nanotube arrays (TNAs) decorated with Au@Pd NPs exhibit superior performance with the Pd shell serving as a catalytic reactor that efficiently extracts hot carriers from the plasmonic Au antenna. The photocatalytic performance in PEC measurements increased with higher Pd coverage, and Au70@Pd30/TNAs exhibited a 2.2-fold higher photocurrent compared with bare Au/TNAs. The enhanced oxygen evolution reaction (OER) activity observed for Au70@Pd30/TNAs is attributed to the higher population of hot holes on the surface of Au@Pd NPs, which enhances the oxidation capability for interactions with electrolytes. Femtosecond transient absorption (fs-TA) spectra of Au@Pd NPs revealed a shorter lifetime of hot electrons through electron-phonon (e-p) scattering in Au70@Pd30 NPs compared to Au NPs, indicating suppressed charge recombination and increased hot hole population on the surface. Therefore, this study suggests that the plasmonic antenna-reactor model, critically influenced by hot carrier dynamics, provides a promising framework for efficient photoelectrocatalytic systems.
SAM3X: Efficient 3D-Aware Network for Medical Image Segmentation Using SAM
No full textRecent studies have adopted the Segment Anything Model (SAM), a vision foundation model, for medical image analysis, demonstrating its generalizability and superior performance. However, since most research focuses on slice-by-slice approaches, the potential of pre-trained SAM for 3D medical images remains underexplored. Furthermore, achieving consistent segmentation across slices is challenging due to the need for expert-provided prompts. To alleviate these issues, we propose a novel lightweight network, SAM3X, to fully leverage the ability of pre-trained SAM in 3D medical image segmentation. SAM3X employs the SAM encoder across three axes to obtain robust and rich representations of 3D volume images. Also, we implement SAM3X in a prompt-free manner and employ lightweight decoders with fine-grained skip connection to capture inter-slice information in various aspects while keeping the network efficient in parameters. Through extensive experiments on various datasets, we verify the effectiveness and efficiency compared to other SAM-based approaches and vision transformer methods. https://github.com/SSTDV-Project/SAM3X
Fokker–Planck-Based Kinetic Models for Rarefied Gas Flows: Extending to Diatomic Mixtures and Achieving Second-Order Accuracy
No full textAtomically dispersed silver on nanosheet-stacked amorphous alumina for enhanced NOx reduction
No full textTailoring anchoring sites in Al2O3 to strengthen metal-support interactions (MSI) remains a challenge yet essential for designing robust single-atom catalysts. Herein, nanosheet-stacked amorphous alumina (mAl2O3) with enhanced surface area and abundant hydroxyl groups was prepared by controlling the thermal treatment protocol of an Al-containing MOF. Loading 1 wt% Ag onto mAl2O3 led to atomically dispersed Ag species due to strong MSI, enabled by effective anchoring onto terminal hydroxyl groups uniquely distributed on mAl2O3, where both OH-mu 1-AlIV and OH-mu 1-AlVI sites coexist, unlike conventional Al2O3, predominantly featuring OH-mu 1-AlVI alone. In hydrocarbon selective catalytic reduction, Ag(1)/mAl2O3 demonstrated markedly higher deNOx activity, exceptional water resistance, and durability. Detailed surface studies confirmed the facilitated formation of reactive enolic species and surface nitrates (NO3-) on Ag(1)/mAl2O3, which accelerated isocyanate (-NCO) formation, the key intermediate for selective NOx reduction. This work introduces an innovative method to produce Al2O3 with tunable surface structures, fostering highly active single-atom catalysts.
The Cosmic Infrared Background Experiment-2: An Intensity-mapping Optimized Sounding-rocket Payload to Understand the Near-IR Extragalactic Background Light
No full textThe background light produced by emission from all sources over cosmic history is a powerful diagnostic of structure formation and evolution. At near-infrared wavelengths, this extragalactic background light (EBL) is comprised of emission from galaxies stretching all the way back to the first-light objects present during the Epoch of Reionization. The Cosmic Infrared Background Experiment 2 (CIBER-2) is a sounding-rocket experiment designed to measure both the absolute photometric brightness of the EBL over 0.5-2.0 mu m and perform an intensity-mapping measurement of EBL spatial fluctuations in six broad bands over the same wavelength range. CIBER-2 comprises a 28.5 cm, 80 K telescope that images several square degrees to three separate cameras. Each camera is equipped with an HAWAII-2RG detector covered by an assembly that combines two broadband filters and a linear-variable filter, which perform the intensity mapping and absolute photometric measurements, respectively. CIBER-2 has flown three times: an engineering flight in 2021, a terminated launch in 2023, and a successful science flight in 2024. In this paper, we review the science case for the experiment; describe the factors motivating the instrument design; review the optical, mechanical, and electronic implementation of the instrument; present preflight laboratory characterization measurements; and finally assess the instrument's performance in flight.
Disease-Disease Interactions: Molecular Links of Neurodegenerative Diseases with Cancer, Viral Infections, and Type 2 Diabetes
No full textNeurodegenerative disorders, notably Alzheimer’s and Parkinson’s diseases, are unified by progressive neuronal loss and aberrant protein aggregation. Growing evidence indicates that these conditions are linked to cancer, infectious diseases, and type 2 diabetes through convergent molecular processes. In this review, we examine the mechanistic foundations of these links, focusing on shared features such as protein misfolding and aggregation, chronic inflam‑mation, and dysregulated signalling pathways. We integrate cellular, animal, and human data to illustrate how patho‑genic proteins may influence one another through cross-seeding and co-aggregation, and assess the implications of such interactions for disease susceptibility, progression, and treatment response. Understanding these underlying mechanisms may provide a conceptual framework for developing therapeutic approaches that target the molecular basis of multiple complex disorders.
High Aspect Ratio Silicon Nanohole Arrays via Electric-Field-Incorporated Metal-Assisted Chemical Etching
No full textThe implementation of through-silicon vias for high-performance semiconductor devices requires a reliable fabrication process that can achieve high aspect ratio (HAR) silicon nanoholes (Si NHs). Currently, Si NHs are primarily fabricated via plasma-based dry etching, which has technical limitations, such as necking and bowing. Metal-assisted chemical etching (MaCE) is an alternative Si NH fabrication method that utilizes wet chemistry catalyzed by metals. However, the formation of HAR Si NHs is challenging because of the unstable motion of metal catalysts during MaCE. Herein, we introduce electric-field-incorporated MaCE (EMaCE) to improve the anisotropic etching stability of metal catalysts and achieve the formation of Si NHs. The etch straightness gradually improved with increasing electric field intensity while the etch rate remained nearly constant. We optimized the etchant concentration and etch time to increase the etch rate, and thus, fabricated an ultra-HAR (38:1) Si NHs array via EMaCE.