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With the increasing integration levels of modern electronic products, effective thermal management has become a critical concern to ensure the operational efficiency and longevity of electronic devices. Developing thermal interface materials that possess both high thermal conductivity (κ) and reliable insulation properties has presented a significant challenge. In this study, boron nitride fibers (BNF) with a substantial length-to-diameter ratio were fabricated to serve as vertically aligned thermal conduction channels within a composite pad containing a silicone gel matrix.Under typical packing pressure conditions, the resulting BNF pad exhibits an exceptionally high through-plane thermal conductivity (40 vol. %: κ⊥ = 16.77 W/mK). Additionally, the BNF pad demonstrates favorable mechanical and dielectric properties, enhancing its versatility and applicability. The results of actual heat dissipation tests further validate the excellent thermal management capabilities of these composites in high-power electronic applications.

This paper introduces a novel approach, namely Variable-Periodic Voronoi Tessellation (VPVT), for the bio-inspired design of porous structures. The method utilizes distributed points defined by a variable-periodic function to generate Voronoi tessellation patterns, aligning with a wide diversity of artificial or natural cellular structures. In this VPVT design method, the truss-based architecture can be fully characterized by design variables, such as frequency factors, thickness factors. This approach enables the optimal design of porous structures for both mechanical performance and functionality. The varied, anisotropic cell shapes and sizes of VPVT porous structures provide significantly greater design flexibility compared to typical isotropic porous structures. In addition, the VPVT method not only can design micro-macro multiscale materials, but is also applicable for the design of meso-macro scale truss-based porous structures, such as architecture constructions, biomedical implants, and aircraft frameworks. This work employs a Surrogate-assisted Differential Evolution (SaDE) method to perform the optimization process. Numerical examples and experiments validate that the proposed design achieves about 51.1% and 47.8% improvement in compliance performance and damage strength, respectively, than existing studies.

The sustainable production of chemicals and fuels from waste carbon sources holds significant promise for reducing our dependence on fossil resources. Here, the authors demonstrate renewable formate production from biomass wastes and CO2 powered by solar energy.

Abstract It is widely adopted in thermal metamaterials that mixing different materials could conveniently result in effective thermal conductivities (ETCs) beyond naturally-occurring materials. When multiple materials are isotropically mixed, the ETC is a direct average governed by their filling fractions and given bulk conductivities. That could lead to an inhomogeneous and anisotropic value within the maximal and minimal thermal conductivities of constituent materials. Usually thermal metadevices rely on anisotropic thermal conductivity tensor, whose tensorial elements are frequently inter-dependent and confined within a limited parametric space. It is thus nontrivial to establish a design recipe for advanced thermal metamaterials whose ETCs could cover full-parameter anisotropic space. We demonstrate topological functional cells (TFCs) with copper and polydimethylsiloxane, and show that the anisotropic ETCs traverse their full-parameter space. Such robust scheme based on topology-optimized TFCs unlocks unexplored opportunities for functional thermal metadevices whose parameters may not be reached in previous mixing approaches. This study also sheds light on the developments in emerging acoustic, mechanical and electromagnetic composite materials.

The synthesis of renewable chemical fuels from CO2 and H2O via photoelectrochemical (PEC) route reprensents a promising room-temperature approach for transforming greenhouse gas into value-added chemicals (e.g., syngas), but to date it has been hampered by the lack of efficient photocathode for CO2 reduction. Herein, we report efficient PEC CO2 reduction into syngas by photocathode engineering. The photocathode is consisting of a planar p-n Si junction for strong light harvesting, GaN nanowires for efficient electron extraction and transfer, and Au/TiO2 for rapid electrocatalytic syngas production. The photocathode yields a record-high solar energy conversion efficiency of 2.3%. Furthermore, desirable syngas compositions with CO/H2 ratios such as 1:2 and 1:1 can be produced by simply varying the size of Au nanoparticle. Theoretical calculations reveal that the active sites for CO and H2 generation are the facet and undercoordinated sites of Au particles, respectively.

Summary: Photoelectrochemical CO2 reduction into syngas (a mixture of CO and H2) provides a promising route to mitigate greenhouse gas emissions and store intermittent solar energy into value-added chemicals. Design of photoelectrode with high energy conversion efficiency and controllable syngas composition is of central importance but remains challenging. Herein, we report a decoupling strategy using dual cocatalysts to tackle the challenge based on joint computational and experimental investigations. Density functional theory calculations indicate the optimization of syngas generation using a combination of fundamentally distinctive catalytic sites. Experimentally, by integrating spatially separated dual cocatalysts of a CO-generating catalyst and a H2-generating catalyst with GaN nanowires on planar Si photocathode, we report a record high applied bias photon-to-current efficiency of 1.88% and controllable syngas products with tunable CO/H2 ratios (0–10) under one-sun illumination. Moreover, unassisted solar CO2 reduction with a solar-to-syngas efficiency of 0.63% is demonstrated in a tandem photoelectrochemical cell.

Sunlight-harvesting materials require the clean integration of light-absorbing and catalytic components to be efficient. Here, authors link silicon photoelectrodes and molybdenum sulfide catalysts with defect-free gallium nitride nanowire to improve photoelectrochemical hydrogen evolution.

Nanowire-microfluidic strain sensors: a stretchable multi-scale sensing solution By combining metal nanowires and conductive polymers, high-performance stretchable nanowire-microfluidic strain sensors are realized. A team lead by Chuan Liu from the School of Electronics and Information Technology at Sun Yat-Sen University developed a hybrid strain sensor consisting of brittle metal nanowires and conductive polymers. These robust nanowire-microfluidic strain sensors are sensitive to multiscale strains––from 4% to over 400%––and show record-high gauge factor, a figure-of-merit that quantifies the level of sensitivity. The hybrid strain sensor’s high performance is made possible by the electric percolation pathways formed between the parallel nanowire network and the microfluidic channels. The combination of high stretchability and high sensitivity over a large strain range enables the device to be suitable for multiscale sensing. Liu and coworkers demonstrate the applicability of their nanowire-microfluidic strain sensors to human motion detection and human–machine interactive systems.

Our study presents a 319-gene panel targeting inherited retinal dystrophy (IRD) genes. Through a multi-center retrospective cohort study, we validated the assays effectiveness and clinical utility and characterized the mutation spectrum of Taiwanese IRD patients. Between January 2018 and May 2022, 493 patients in 425 unrelated families, all initially suspected of having IRD without prior genetic diagnoses, underwent detailed ophthalmic and physical examinations (with extra-ocular features recorded) and genetic testing with our customized panel. Disease-causing variants were identified by segregation analysis and clinical interpretation, with validation via Sanger sequencing. We achieved a read depth of >200× for 94.2% of the targeted 1.2 Mb region. 68.5% (291/425) of the probands received molecular diagnoses, with 53.9% (229/425) resolved cases. Retinitis pigmentosa (RP) is the most prevalent initial clinical impression (64.2%), and 90.8% of the cohort have the five most prevalent phenotypes (RP, cone-rod syndrome, Ushers syndrome, Lebers congenital amaurosis, Bietti crystalline dystrophy). The most commonly mutated genes of probands that received molecular diagnosis are USH2A (13.7% of the cohort), EYS (11.3%), CYP4V2 (4.8%), ABCA4 (4.5%), RPGR (3.4%), and RP1 (3.1%), collectively accounted for 40.8% of diagnoses. We identify 87 unique unreported variants previously not associated with IRD and refine clinical diagnoses for 21 patients (7.22% of positive cases). We developed a customized gene panel and tested it on the largest Taiwanese cohort, showing that it provides excellent coverage for diverse IRD phenotypes.

Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks based on Bayesian compressive sensing and particle swarm optimization. The algorithm we proposed is a two-tier PSO approach. In the first tier, a PSO-based clustering protocol is proposed to synthetically consider the energy consumption and uniformity of cluster head distribution. Then in the second tier, a PSO-based routing protocol is proposed to implement inner-cluster one-hop routing and outer-cluster multi-hop routing. The nodes selected to constitute i-th effective routing path decide which positions in the i-th row of the measurement matrix are nonzero. As a result, in this tier the protocol comprehensively considers energy efficiency, network balance and data recovery quality. The Bayesian Cramér-Rao Bound (BCRB) in such a case is analyzed and added in the fitness function to monitor the mean square error of the reconstructed signal. The experimental results validate that our algorithm maintains a longer life time and postpones the appearance of the first dead node while keeps the reconstruction error lower compared with the cutting-edge algorithms which are also based on distributed multi-hop compressive sensing approaches.

Introduction: Anti-vascular endothelial growth factor (VEGF) treatment stands as the primary approach for neovascular age-related macular degeneration (nAMD). Faricimab has recently emerged as a novel anti-VEGF option for nAMD. This study aimed to assess the efficacy of faricimab in patients with refractory nAMD. Method: This retrospective study focused on refractory nAMD patients treated with faricimab at Taipei Veterans General Hospital from March 2023 to December 2023. Primary outcomes assessed the change in mean best-corrected visual acuity (BCVA) and central retinal thickness (CRT) over the first 4 months. Secondary outcomes included the presence of subretinal and intraretinal fluid (SRF and IRF) and changes in pigment epithelial detachment (PED). Subgroup analysis for the successful and unsuccessful treatment groups was conducted to identify potential confounding factors influencing treatment response. Result: This study included 42 eyes with refractory nAMD treated with faricimab. During a 6-month follow-up, no significant improvement in BCVA was observed, while CRT significantly decreased at all time points, except during the 5-month follow-up. Height PED showed significant reduction up to 5 months. The prevalence of SRF decreased significantly, while IRF remained lower but not significant. According to the treatment criteria, 67.4% successfully met the treatment goals. Subgroup analysis between successful and unsuccessful groups showed no significant differences in baseline characteristics, except a higher predominantly serous PED percentage in the successful group. Conclusion: Faricimab showed favorable outcomes in refractory nAMD patients. Further investigations are needed to understand the factors contributing to its efficacy. [ABSTRACT FROM AUTHOR]