Toward controllable and predictable synthesis of high-entropy alloy nanocrystals.

High-entropy alloy (HEA) nanocrystals have attracted extensive attention in catalysis. However, there are no effective strategies for synthesizing them in a controllable and predictable manner. With quinary HEA nanocrystals made of platinum-group metals as an example, we demonstrate that their structures with spatial compositions can be predicted by quantitatively knowing the reduction kinetics of metal precursors and entropy of mixing in the nanocrystals under dropwise addition of the mixing five-metal precursor solution. The time to reach a steady state for each precursor plays a pivotal role in determining the structures of HEA nanocrystals with homogeneous alloy and core-shell features. Compared to the commercial platinum/carbon and phase-separated counterparts, the dendritic HEA nanocrystals with a defect-rich surface show substantial enhancement in catalytic activity and durability toward both hydrogen evolution and oxidation. This quantitative study will lead to a paradigm shift in the design of HEA nanocrystals, pushing away from the trial-and-error approach.

Rapid and Highly Sensitive Detection of C-Reaction Protein Using Robust Self-Compensated Guided-Mode Resonance BioSensing System for Point-of-Care Applications.

The rapid and sensitive detection of human C-reactive protein (CRP) in a point-of-care (POC) may be conducive to the early diagnosis of various diseases. Biosensors have emerged as a new technology for rapid and accurate detection of CRP for POC applications. Here, we propose a rapid and highly stable guided-mode resonance (GMR) optofluidic biosensing system based on intensity detection with self-compensation, which substantially reduces the instability caused by environmental factors for a long detection time. In addition, a low-cost LED serving as the light source and a photodetector are used for intensity detection and real-time biosensing, and the system compactness facilitates POC applications. Self-compensation relies on a polarizing beam splitter to separate the transverse-magnetic-polarized light and transverse-electric-polarized light from the light source. The transverse-electric-polarized light is used as a background signal for compensating noise, while the transverse-magnetic-polarized light is used as the light source for the GMR biosensor. After compensation, noise is drastically reduced, and both the stability and performance of the system are enhanced over a long period. Refractive index experiments revealed a resolution improvement by 181% when using the proposed system with compensation. In addition, the system was successfully applied to CRP detection, and an outstanding limit of detection of 1.95 × 10-8 g/mL was achieved, validating the proposed measurement system for biochemical reaction detection. The proposed GMR biosensing sensing system can provide a low-cost, compact, rapid, sensitive, and highly stable solution for a variety of point-of-care applications.

Performance of sparse-view CT reconstruction with multi-directional gradient operators.

To further reduce the noise and artifacts in the reconstructed image of sparse-view CT, we have modified the traditional total variation (TV) methods, which only calculate the gradient variations in x and y directions, and have proposed 8- and 26-directional (the multi-directional) gradient operators for TV calculation to improve the quality of reconstructed images. Different from traditional TV methods, the proposed 8- and 26-directional gradient operators additionally consider the diagonal directions in TV calculation. The proposed method preserves more information from original tomographic data in the step of gradient transform to obtain better reconstruction image qualities. Our algorithms were tested using two-dimensional Shepp-Logan phantom and three-dimensional clinical CT images. Results were evaluated using the root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and universal quality index (UQI). All the experiment results show that the sparse-view CT images reconstructed using the proposed 8- and 26-directional gradient operators are superior to those reconstructed by traditional TV methods. Qualitative and quantitative analyses indicate that the more number of directions that the gradient operator has, the better images can be reconstructed. The 8- and 26-directional gradient operators we proposed have better capability to reduce noise and artifacts than traditional TV methods, and they are applicable to be applied to and combined with existing CT reconstruction algorithms derived from CS theory to produce better image quality in sparse-view reconstruction.

Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications.

Limited-angle iterative reconstruction (LAIR) reduces the radiation dose required for computed tomography (CT) imaging by decreasing the range of the projection angle. We developed an image-quality-based stopping-criteria method with a flexible and innovative instrument design that, when combined with LAIR, provides the image quality of a conventional CT system. This study describes the construction of different scan acquisition protocols for micro-CT system applications. Fully-sampled Feldkamp (FDK)-reconstructed images were used as references for comparison to assess the image quality produced by these tested protocols. The insufficient portions of a sinogram were inpainted by applying a context encoder (CE), a type of generative adversarial network, to the LAIR process. The context image was passed through an encoder to identify features that were connected to the decoder using a channel-wise fully-connected layer. Our results evidence the excellent performance of this novel approach. Even when we reduce the radiation dose by 1/4, the iterative-based LAIR improved the full-width half-maximum, contrast-to-noise and signal-to-noise ratios by 20% to 40% compared to a fully-sampled FDK-based reconstruction. Our data support that this CE-based sinogram completion method enhances the efficacy and efficiency of LAIR and that would allow feasibility of limited angle reconstruction.

Compressed sensing based CT reconstruction algorithm combined with modified Canny edge detection.

Given that the computed tomography (CT) reconstruction algorithm based on compressed sensing (CS) results in blurred edges, we propose a modified Canny operator that assists the CS algorithm to accurately capture an object's edge, to preserve and further enhance the contrasts in the reconstructed image, thereby improving image quality. We modified two procedures of the traditional Canny operator, namely non-maximum suppression and edge tracking by hysteresis according to the characteristics of low-dose CT reconstruction, and proposed two major modifications: double-response edge detection and directional edge tracking. The newly modified Canny operator was combined with the CS reconstruction algorithm to become an edge-enhanced CS (EECS). Both a 2D Shepp-Logan phantom and a 3D dental phantom were used to conduct reconstruction testing. Root-mean-square error, peak signal-to-noise ratio, and universal quality index were employed to verify the reconstruction results. Qualitative and quantitative results of EECS reconstruction showed its superiority over conventional CS or CS combined with different edge detection techniques, such as Laplacian, Prewitt, Sobel operators, etc. The experiments verified that the proposed modified Canny operator is able to effectively detect the edge location of an object during low-dose reconstruction, enabling EECS to reconstruct images with better quality than those produced by other algorithms.