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The structured processing of graphite is complex and challenging, in which expanded graphite plays a crucial role. Given its superior physical and chemical properties, expanded graphite finds extensive application in diverse domains such as electrochemistry and thermal management. However, the traditional preparation process is inconvenient in effectively meeting the design requirements on the macro and micro scales, which presents a challenge for the structured processing of expanded graphite materials. Here, an innovative method is first proposed for the controllable preparation of expanded graphite microspheres. Inspired by the explosion process of popcorn, the controlled gas release inside the natural flake graphite during chemical expansion is regulated by fuming sulfuric acid, realizing the controllable preparation of expanded graphite microspheres. Subsequently, sulfur trioxide can also intensify the degree of oxidation on the surface of the microspheres. The controllable microsphere morphology endows the composite with good isotropic network bonding, with considerable thermal conductivity of 1.703 W m−1 K−1 at low loading of 10 wt.% and reliable cyclic stability. This work opens up a new way for the morphology control of expanded graphite and provides a novel design thought for the physical and chemical structure control of carbon materials in the future. [ABSTRACT FROM AUTHOR]

Landmarks are essential in non-rigid shape registration for identifying the correspondence between designs and actual products. In 3D printing, manual selection of landmarks becomes labor-intensive, due to complex product geometries and their non-uniform shape deviations. Automatic selection, however, has to pinpoint landmarks indicative of geometric regions prone to deviations for accuracy qualification. Existing automatic landmarking methods often generate clustered and redundant landmarks for prominent features with high curvatures, compromising the balance between global and local registration errors. To address these issues, we propose an automatic landmark selection method through deviation-aware segmentation and landmarking. As opposed to segmentation for semantic feature identification, deviation-aware segmentation partitions a freeform product for high-curvature region identification. Prone to deviation, these regions are generated through curvature-sensitive remeshing to extract vertices of high curvature and automatic clustering of vertices based on vertex density. Within each segment or high-curvature region, a curvature-weighted function is tailored for the Gaussian process landmarking to sequentially select landmarks with the highest local curvatures. Furthermore, we propose a new evaluation criterion to assess the effectiveness of selected landmarks through registration. The proposed approach is tested through automatic landmarking of printed dental models. [ABSTRACT FROM AUTHOR]

Multielectrode electrochemical biosensors promise on-the-spot inspection of target compounds in biofluids, reducing costs in personalized healthcare. However, sensor sensitivity may decrease after each use due to biofouling, where chemical attachments on sensor electrodes curtail sensing signals. Current biofouling characterization techniques rely on time-consuming offline tests and analysis, making them impractical for on-the-spot signal correction. Alternatively, we propose to statistically model and correct the biofouling-induced signal changes. However, in addition to biofouling, the signals are influenced by multiple sources of variation, each with different levels of impact. To effectively characterize and separate biofouling effects from the major sources of variability, we establish a multiresolution functional mixed-effect model based on domain knowledge. A biosensing signal is first decomposed into a smooth trend and local peaks. The smooth trend models the effects of population-level biofluid composition, as well as patient and electrode effects to isolate variability sources. Changes in local peak location and amplitude indicate biofouling. These local peaks are modeled using a sparse subset of high-order functional terms. By modeling the changes of those high-order terms, we can characterize and predict the biofouling between consecutive measurements. We propose a sequential parameter estimation procedure that ensures model identifiability. A nonparametric regression model is developed for biofouling prediction. The proposed strategy is validated through simulation and real case studies, effectively correcting biofouling-affected signals from new patients. [ABSTRACT FROM AUTHOR]