Synthesis of nitrogen-doped amorphous carbon nanotubes from novel cobalt-based MOF precursors for improving potassium-ion storage capability. - In: , ISSN 1095-7103, Bd. 677 (2025), 1, S. 35-44
Amorphous carbon materials with sophisticated morphologies, variable carbon layer structures, abundant defects, and tunable porosities are favorable as anodes for potassium-ion batteries (PIBs). Synthesizing amorphous carbon materials typically involves the pyrolysis of carbonaceous precursors. Nonetheless, there is still a lack of studies focused on achieving multifaceted structural optimizations of amorphous carbon through precursor formulation. Herein, nitrogen-doped amorphous carbon nanotubes (NACNTs) are derived from a novel composite precursor of cobalt-based metal-organic framework (CMOF) and graphitic carbon nitride (g-CN). The addition of g-CN in the precursor optimizes the structure of amorphous carbon such as morphology, interlayer spacing, nitrogen doping, and porosity. As a result, NACNTs demonstrate significantly improved electrochemical performance. The specific capacities of NACNTs after cycling at current densities of 100 mA/g and 1000 mA/g increased by 194 % and 230 %, reaching 346.6 mAh/g and 211.8 mAh/g, respectively. Furthermore, the NACNTs anode is matched with an organic cathode for full-cell evaluation. The full-cell attains a high specific capacity of 106 mAh/gcathode at a current density of 100 mA/g, retaining 90.5 % of the specific capacity of the cathode half-cell. This study provides a valuable reference for multifaceted structural optimization of amorphous carbon to improve potassium-ion storage capability.
https://doi.org/10.1016/j.jcis.2024.07.191
Interplay of acoustophoresis and dielectrophoresis in a standing surface acoustic wave field: from spherical to non-spherical particles. - In: , ISSN 1613-4990, Bd. 28 (2024), 10, 67, S. 1-20
Standing surface acoustic waves (sSAW) emerged as a flexible tool for precise manipulation of spherical and non-spherical objects in Lab-on-a-Chip devices. While the manipulation of suspended particles and cells in acoustofluidic devices is mostly dominated by acoustic forces due to acoustic scattering and the acoustically induced fluid flow, surface acoustic waves are inherently linked to an inhomogeneous electric field. The superimposed effects of dielectrophoretic forces and torques on polarizable particles are less explored in microfluidics using sSAW. In this study, a thorough analysis of the physical interplay of acoustophoresis and dielectrophoresis aims to bridge this gap. In comprehensive experiments, the dielectrophoretic impact on the behavior of spherical and non-spherical particles is distinguished by screening the electric field of the sSAW inside the micro channel locally. As a result, particles are forced into trapping locations across the entire channel height. However, the height position close to the bottom differs between the screened and non-screened region. Regardless of the shape of the particles used in this study, particles are forced towards the bottom at the region with screening, while being levitated at regions without screening. This indicates clearly the influence of the electric field in close vicinity to the substrate surface. Furthermore, the unintuitive preferred orientation of prolate spheroids perpendicular to the pressure nodes of the sSAW recently reported, is confirmed in both region regardless of the presence of the electric field. Based on a three-dimensional numerical model, this orientation results not only due to the acoustic torque but is also caused by the dielectrophoretic torque, which complement each other. The experimental and numerical findings are in excellent agreement and provide deep insights into the underlying physical mechanisms responsible for patterning and orientation of the particles.
https://doi.org/10.1007/s10404-024-02762-8
Feature-based dataset fingerprinting for Clustered Federated Learning on medical image data. - In: , ISSN 1087-6545, Bd. 38 (2024), 1, e2394756, insges. 21 S.
Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility because data is not independent and identically distributed (non-IID). Recent work has proposed to cluster clients according to dataset fingerprints to improve model utility in such situations. These fingerprints aim to capture the key characteristics of clients’ local data distributions. Recently, a mechanism was proposed to calculate dataset fingerprints from raw client data. We find that this fingerprinting mechanism comes with substantial time and memory consumption, limiting its practical use to small datasets. Additionally, shared raw data fingerprints can directly leak sensitive visual information, in certain cases even resembling the original client training data. To alleviate these problems, we propose a Feature-based dataset FingerPrinting mechanism (FFP). We use the MedMNIST database to develop a highly realistic case study for FL on medical image data. Compared to existing methods, our proposed FFP reduces the computational overhead of fingerprint calculation while achieving similar model utility. Furthermore, FFP mitigates the risk of raw data leakage from fingerprints by design.
https://doi.org/10.1080/08839514.2024.2394756
Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry. - In: Journal of biomedical optics, ISSN 1560-2281, Bd. 29 (2024), S3, S33309, S. S33309-1-S33309-20
Significance: Monitoring oxygen saturation (SpO2) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications of SpO2 measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions. Aim: We aim to develop and validate a contactless SpO2 measurement approach using 3D convolutional neural networks (3D CNN) and 3D visible-near-infrared (VIS-NIR) multimodal imaging, to offer a convenient, accurate, and robust alternative for SpO2 monitoring. Approach: We propose an approach that utilizes a 3D VIS-NIR multimodal camera system to capture facial videos, in which SpO2 is estimated through 3D CNN by simultaneously extracting spatial and temporal features. Our approach includes registration of multimodal images, tracking of the 3D region of interest, spatial and temporal preprocessing, and 3D CNN-based feature extraction and SpO2 regression. Results: In a breath-holding experiment involving 23 healthy participants, we obtained multimodal video data with reference SpO2 values ranging from 80% to 99% measured by pulse oximeter on the fingertip. The approach achieved a mean absolute error (MAE) of 2.31% and a Pearson correlation coefficient of 0.64 in the experiment, demonstrating good agreement with traditional pulse oximetry. The discrepancy of estimated SpO2 values was within 3% of the reference SpO2 for ∼80% of all 1-s time points. Besides, in clinical trials involving patients with sleep apnea syndrome, our approach demonstrated robust performance, with an MAE of less than 2% in SpO2 estimations compared to gold-standard polysomnography. Conclusions: The proposed approach offers a promising alternative for non-contact oxygen saturation measurement with good sensitivity to desaturation, showing potential for applications in clinical settings.
https://doi.org/10.1117/1.JBO.29.S3.S33309
Exact output tracking in prescribed finite time via funnel control. - In: Automatica, ISSN 0005-1098, Bd. 170 (2024), 111873, S. 1-9
Output reference tracking of unknown nonlinear systems is considered. The control objective is exact tracking in predefined finite time, while in the transient phase the tracking error evolves within a prescribed boundary. To achieve this, a novel high-gain feedback controller is developed that is similar to, but extends, existing high-gain feedback controllers. Feasibility and functioning of the proposed controller is proven rigorously. Examples for the particular control objective under consideration are, for instance, linking up two train sections, or docking of spaceships.
https://doi.org/10.1016/j.automatica.2024.111873
Is open access disrupting the journal business? : a perspective from comparing full adopters, partial adopters, and non-adopters. - In: Journal of informetrics, ISSN 1751-1577, Bd. 18 (2024), 4, 101574, S. 1-14
Two decades after the inception of open access publishing (OA), its impact has remained a focal point in academic discourse. This study adopted a disruptive innovation framework to examine OA's influence on the traditional subscription market. It assesses the market power of gold journals (OA full adopters) in comparison with hybrid journals and closed-access journals (partial adopters and non-adopters). Additionally, it contrasts the market power between hybrid journals (partial adopters) and closed-access journals (non-adopters). Using the Lerner index to measure market power through price elasticity of demand, this study employs difference tests and multiple regressions. These findings indicate that OA full adopters disrupt the market power of non-adopting incumbents. However, by integrating the OA option into their business models, partial adopters can effectively mitigate this disruption and expand their influence from the traditional subscription market to the emerging OA paradigm.
https://doi.org/10.1016/j.joi.2024.101574
Accelerating innovation in 6G research: real-time capable SDR system architecture for rapid prototyping. - In: IEEE access, ISSN 2169-3536, Bd. 12 (2024), S. 118718-118732
The upcoming 3GPP global mobile communication standard 6G strives to push the technological limits of radio frequency (RF) communication even further than its predecessors: Sum data rates beyond 100 Gbit/s, RF bandwidths above 1 GHz per link, and sub-millisecond latency necessitate very high performance development tools. We propose a new SDR firmware and software architecture designed explicitly to meet these challenging requirements. It relies on Ethernet and commercial off-the-shelf network and server components to maximize flexibility and to reduce costs. We analyze state-of-the-art solutions (USRP X440 and other RFSoC-based systems), derive architectural design goals, explain resulting design decision in detail, and exemplify our architecture’s implementation on the XCZU48DR RFSoC. Finally, we validate its performance via measurements and outline how the architecture surpasses the state of the art with respect to sustained RF recording, while maintaining high Ethernet bandwidth efficiency. Building a 6G integrated sensing and communication (ISAC) example, we demonstrate its real-time and rapid application development capabilities.
https://doi.org/10.1109/ACCESS.2024.3447884
Energy stability analysis of MHD flow in a rectangular duct. - In: Proceedings in applied mathematics and mechanics, ISSN 1617-7061, Bd. 0 (2024), 0, e202400041, S. 1-9
We study the energy stability of pressure-driven laminar magnetohydrodynamic flow in a rectangular duct in the presence of a transverse homogeneous magnetic field. The walls of the duct are electrically insulating. The quasistatic approximation of the induction equation is used. For sufficiently strong fields, the laminar velocity distribution has a uniform core and convex Hartmann and Shercliff boundary layers on the walls perpendicular and parallel to the magnetic field. The linear eigenvalue problem for the energy Reynolds number depends on the streamwise wavenumber, the Hartmann number, and the aspect ratio. We focus on duct geometries with small aspect ratios in order to compare with stability results from one-dimensional channel flow. The lift-up mechanism dominates in the limit of zero streamwise wavenumber and provides a linear dependence between the critical Reynolds and Hartmann number in the duct. For finite streamwise wavenumbers and decreasing aspect ratio, the duct results converge to Orr's original energy stability result for spanwise uniform perturbations to the plane Poiseuille base flow.
https://doi.org/10.1002/pamm.202400041
Light-induced degradation transition energy barrier measured by photoluminescence spectra in Si:In. - In: Physica status solidi, ISSN 1862-6319, Bd. 0 (2024), 0, 2400570, S. 1-7
https://doi.org/10.1002/pssa.202400570
Supervised probabilistic dynamic-controlled latent-variable model for quality pattern prediction and optimisation. - In: ISA transactions, ISSN 1879-2022, Bd. 0 (2024), 0, S. 1-19
A supervised probabilistic dynamic-controlled latent-variable (SPDCLV) model is proposed for online prediction, as well as real-time optimisation of process quality indicators. Compared to existing probabilistic latent-variable models, the key advantage of the proposed method lies in explicitly modelling the dynamic causality from the manipulated inputs to the quality pattern. This is achieved using a well-designed, dynamic-controlled Bayesian network. Furthermore, the algorithms for expectation-maximisation, forward filtering, and backward smoothing are designed for learning the SPDCLV model. For engineering applications, a framework for pattern-based quality prediction and optimisation is proposed, under which the pattern-filtering and pattern-based soft sensor are explored for online quality prediction. Furthermore, quality optimisation can be realised by directly controlling the pattern to the desired condition. Finally, case studies on both an industrial primary milling circuit and a numerical example illustrate the benefits of the SPDCLV method in that it can fully model the process dynamics, effectively predict and optimise the quality indicators, and monitor the process.
https://doi.org/10.1016/j.isatra.2024.08.001