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Super-resolution microscopy and empirically validated autocorrelation image analysis discriminates microstructures of dairy derived gels
摘要： The food industry must capitalise on advancing technologies in order to optimise the potential from emerging ingredient technologies. These can aid in product optimisation and provide quantitative empirical data to which there is a fundamental physical understanding. Super-resolution microscopy provides a tool to characterise the microstructure of complex colloidal materials under near native conditions. Coherent Anti-Stokes Raman Scattering (CARS) microscopy was used to show the presence of fluorescent dye required for imaging does not affect gel microstructure and super-resolution Stimulated Emission Depletion (STED) microscopy is used to image four dairy derived gels. Image analysis has been developed based on 2D spatial autocorrelation, and a model that extracts parameters corresponding to a typical length of the protein domains and the inter pore distance. The model has been empirically validated through the use of generated images to show the fitting parameters relate to precise physical features. The fractal dimension is extracted from Fourier space analysis. The combination of STED microscopy and image analysis is sensitive enough to significantly differentiate samples based on whether gels were made from fresh or reconstituted milk, and whether gelation was induced through acidification or rennet addition. Rheometry shows that the samples exhibit different macroscopic behaviours, and these differences become increasingly significant with time. Samples can be differentiated earlier in the gelation process with imaging as compared to rheometry. This highlights the potential of STED imaging and image analysis to characterise the size of protein domains, pore spacing and the fractal dimensions of microstructures to aid product optimisation.
关键词： Stimulated Emission Depletion (STED) microscopy,Super-resolution microscopy,Fractal dimension,Coherent Anti-stokes Raman Scattering (CARS) microscopy,2D spatial autocorrelation analysis
更新于2025-09-04 15:30:14
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Total-Ionizing-Dose Response of MoS2 Transistors with ZrO2 and h-BN Gate Dielectrics
摘要： The total-ionizing-dose response of few-layer MoS2 transistors with ZrO2 or h-BN gate dielectrics is investigated under various bias conditions. Defects in MoS2 and surrounding dielectric layers significantly affect radiation-induced trapping. For devices with ZrO2 dielectrics, much larger negative Vth shifts and peak transconductance degradation are observed for irradiation under negative and ground bias than under positive bias. h-BN devices exhibit positive threshold voltage shifts under negative-bias irradiation. For both ZrO2 and h-BN passivated devices, the peak transconductance degradation results from charge trapping at the surface of the MoS2 or in nearby oxides. Changes in defect energy distributions of MoS2 FETs during X-ray irradiation are characterized via temperature-dependent low-frequency noise measurements. Density functional theory calculations are performed to provide insight into the pertinent defects.
关键词： DFT,MoS2 FET,low frequency noise,ZrO2,h-BN,2 dimension,X-ray
更新于2025-09-04 15:30:14
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High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI)
摘要： This work addresses the problem of patch-based image denoising through the unsupervised learning of a probabilistic high-dimensional mixture model on the noisy patches. The model, called HDMI, proposes a full modeling of the process that is supposed to have generated the noisy patches. To overcome the potential estimation problems due to the high dimension of the patches, the HDMI model adopts a parsimonious modeling which assumes that the data live in group-specific subspaces of low dimensionalities. This parsimonious modeling allows us in turn to get a numerically stable computation of the conditional expectation of the image which is applied for denoising. The use of such a model also permits us to rely on model selection tools, such as BIC, to automatically determine the intrinsic dimensions of the subspaces and the variance of the noise. This yields a denoising algorithm that can be used both when the noise level is known and is unknown.
关键词： image denoising,parsimonious mixture model,model selection,high-dimensional clustering,intrinsic dimension estimation,patch-based representation
更新于2025-09-04 15:30:14