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4 results for “plasmonic gratings”
Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis
Background <p>Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention. </p> Methods and Findings <p>A single-molecule fluorescence immunosorbent assay was used to detect the Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity.</p> Conclusions <p>The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.</p>
Second Harmonic Generation from Grating-Coupled Hybrid Plasmon-Phonon Polaritons - Experimental and Simulation Data
<p>Experimental and simulation data for "Second Harmonic Generation from Grating-Coupled Hybrid Plasmon-Phonon Polaritons" by Marcel Kohlmann et al., recently (10/22) accepted for publication in Applied Physics Letters. A preprint is available on the <a href="https://arxiv.org/abs/2209.00375">arXiv</a>. </p> <p>Content:<br> We provide matlab code to generate the figures from the experimental and simulated data.</p> <p>fig1.m: generates fig1c,d of the paper<br> fig2_4.m generates fig 2 and 4<br> fig3.m generates fig3</p> <p>experimental_data.zip contains all experimental data, please unzip before running the scripts<br> comsol_data.zip contains all Comsol simulation output data. Please unzip before running the scripts.</p> <p>The remaining matlab scripts are needed in the process. "passler_epsTarray_generator.m" is also part of the transfer matrix implementation available on <a href="https://doi.org/10.5281/zenodo.7034720">Zenodo</a>.</p> <p>Please contact <a href="mailto:alexander.paarmann@fhi-berlin.mpg.de">Alex Paarmann</a> for any questions.</p>
Applications of machine learning tools for ultra-sensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of tuberculosis
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Dataset for Enhanced Photoelectrochemical Nitrogen Reduction to Ammonia by a Plasmon-Active Au Grating Decorated with gC3N4@MoS2 Heterosystem and Plasmon-Active Nanoparticles
<div> <p>This is a dataset for a paper "Enhanced Photoelectrochemical Nitrogen Reduction to Ammonia by a Plasmon-Active Au Grating Decorated with gC3N4@MoS2 Heterosystem and Plasmon-Active Nanoparticles". All details about the data are included in the readme file.</p> <p> </p> </div> <h2></h2>
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