Supercooling perfusion extends organ-preservation time by maintaining grafts ice-free below 0 °C, but thermal non-uniformity and limited intra-organ temperature observability hinder protocol design, especially at large-organ scales. We developed an anatomically based thermo-fluidic modeling framework for supercooled perfusion of the liver, heart, and kidney in a recirculating multi-organ configuration and validated the model experimentally. Three-dimensional organ geometries from the BodyParts3D repository were combined with a porous-media tissue representation and realistic perfusion boundary conditions to resolve transient intra-parenchymal temperature fields. A self-developed variable-frequency supercooled machine perfusion (MP) platform was used to measure temperatures in porcine livers, hearts, and kidneys using multiple thermocouples placed at anatomically corresponding locations. Simulated temperature trajectories agreed with measurements across organs, with mean absolute errors of 0.24 °C for the liver, 2.63 °C for the heart, and 0.4 °C for the kidney, and reproduced initial cooling followed by progressive approach to the perfusate temperature and stabilization. Spatial temperature maps captured organ-specific gradients consistent with convective heat extraction by perfusate delivery and conductive transport within tissue. Using the validated model, we performed parametric sweeps of the inlet perfusion parameter, perfusate thermophysical properties, and external convective heat-transfer coefficient to quantify their effects on cooling rate and temperature uniformity. Based on quantitative metrics, these parameters were found to influence cooling rate and intra-organ temperature uniformity to different degrees, while the magnitude of improvement differed among organs due to size and vascular characteristics. This study provides a validated, under the tested conditions, tool to predict intra-organ temperature evolution and a guide for thermodynamically optimizing supercooled MP protocols in multi-organ preservation.

Anomalous cooling or heating implies attractive underlying thermal physics, and the Mpemba effect and its inverse are typical examples. Most existing explanations for such phenomena are based on microscopic Markovian models that quantify relaxation using distance measures such as total variation distance or Kullback‑Leibler divergence. Here, we propose a macroscopic network system to observe and analyze the Mpemba effect and its inverse by connecting a thermoelectric module with a body and a reservoir at different initial temperatures. Normal cooling and anomalous cooling can be switched in such a setup, and oscillatory behaviors of temperature, current, and heat flow are found to be the key for achieving the Mpemba effect and its inverse. With an unambiguous definition of the criteria, the occurrence domain of the Mpemba effect is sketched in terms of initial temperature, thermoelectric Figure-of-Merit, and the inductance. This work provides a macroscopic network system to understand the Mpemba effect, and offers a more flexible and dynamic way for thermal management and energy conversion.

Hydrogen is widely recognized as the leading green energy carrier of the 21st century, owing to its diverse production pathways, high combustion energy density, and environmentally benign byproduct: water. However, its wide flammability range (4-75 vol.% in air) and extremely low minimum ignition energy (0.02 mJ) pose significant safety risks across the entire lifecycle of production, storage, transportation, and utilization, necessitating real-time monitoring through highly reliable sensing technologies. Among various hydrogen detection methods, thermal conductivity sensors have attracted considerable attention due to their oxygen-independent operation, broad measuring range, mechanical robustness, and long service lifespan. Despite growing research interest, there remains a notable lack of comprehensive review articles specifically dedicated to thermal conductivity hydrogen sensors (TCHSs) that consolidate the current state of knowledge and guide future research directions. This paper presents a systematic analysis of the working principles and operating modes of TCHSs, introduces key performance parameters, and reviews theoretical models describing the effective thermal conductivity of gas mixtures. The discussion covers representative sensor architectures, gas inlet configurations, and critical environmental factors influencing sensor performance. Furthermore, recent advances and emerging trends are examined, with particular emphasis on smart gas sensing technologies enabled by sensor integration and advanced machine learning algorithms. This study aims to serve as a comprehensive academic reference, offering a clear and structured framework for researchers, particularly those newly entering the field of hydrogen sensing.

This work investigates the transport of an electron bubble near the free surface of superfluid ³He-B under applied electric and magnetic fields. Based on a theoretical framework combining the quasiclassical Green’s function and the Lippmann–Schwinger equation, we have calculated the scattering cross section and mobility of the electron bubble, together with their temperature and depth dependences. An electric field shifts the position of the electron bubble and thereby tunes its coupling to the surface bound states. The surface density of states decays with depth, whereas the transport cross section increases with energy and depth; these competing trends compensate, resulting in a nearly depth-independent mobility consistent with the linear dispersion of the surface states. In contrast, an applied magnetic field opens a Zeeman gap in the surface-state spectrum, which breaks the linear dispersion of the bound states. Our results demonstrate that external electric and magnetic fields provide effective control of the spectral structure and scattering properties of the surface bound states.

Driven by the escalating chip-level heat flux demands of artificial intelligence and high-performance computing, thermal management has emerged as a critical bottleneck for next-generation microelectronic integration. To address the prominent contradiction between the limited space and the high heat flux in silicon interconnect fabric chips, this research has overcome the key challenge of miniaturizing traditional spray cooling by designing and implementing an ultra-thin spray cooling heat sink embedded in a silicon-based test chip. The core advancement stems from a synergistic integration of topology-optimized micro-nozzle architecture and silicon-based microfabrication, achieving a total spray module thickness of merely 3.5 mm and enabling uniform near-field atomization from four nozzles under low pressure. Experimental results demonstrate that the heat sink removes 614 W at a junction temperature of 92 °C from a compact footprint of 9.5 mm × 9.5 mm, yielding a peak surface heat transfer coefficient of 9.03 W/(cm²·K). This performance not only validates the feasibility of spray cooling in ultra-thin packaging architectures, but also presents one of the first experimental demonstration of the monolithic integration of a spray cooling system with a silicon-based integrated circuit. This work establishes a viable pathway for ultra-high heat flux thermal management under extreme spatial constraints, enabling the practical deployment of spray cooling in high-power-density electronics, including high-performance computing and artificial intelligence chips.