Jierong Liang

Rare-earth magnetic materials hold significant promise for advanced functional devices, but their additive manufacturing is severely hindered by high chemical reactivity, susceptibility to oxidation, and a narrow processable window. Here, we present a systematic investigation into selective laser melting (SLM) of gadolinium-based rare-earth magnets, covering powder feedstock preparation, multi-scale process optimization, fabrication of complex porous architectures, and system-level performance validation. High-sphericity and high-purity gadolinium powders were first produced via plasma rotating electrode process (PREP), providing qualified feedstock tailored for powder bed fusion. A hierarchical optimization strategy---encompassing single-track, single-layer, and multi-layer experiments---was then established to define a robust processing window for dense part fabrication. Representative porous regenerators were subsequently manufactured to assess the influence of structural complexity on dimensional accuracy and manufacturability. Square-fin structures exhibit excellent geometric fidelity, whereas microchannel counterparts show pronounced dimensional deviations due to inherent limitations of fine-feature SLM processing. The fabricated regenerators were further integrated into an active magnetic refrigeration system for functional testing. Experimental results confirm that structural manufacturing accuracy critically governs the thermofluidic behavior and cooling performance of magnetic refrigeration devices under various operating conditions. Collectively, this work establishes an integrated feedstock--process--structure--application framework for additive manufacturing of rare-earth magnetic materials, bridging the gap between magnetic material process development and practical system deployment. The proposed methodology offers a viable pathway toward industrial-scale production of high-performance functional metallic components.

High-temperature heat pumps (HTHPs) offer a transformative pathway to fully electrify spray drying, yet their deployment remains hindered by prohibitive heat costs, impractically high CO₂-HTHP operating pressures, and process-integration inefficiencies. To address these challenges, we present a greenfield integration concept employing multiple modular HTHPs capable of supplying ~240 °C air, enabling efficient and scalable electrification across diverse milk-powder production volumes. A multiphase approach was employed, comprising: (1) diagram based heat integration quality assessment for preliminary HTHP placements, (2) detailed process simulation and exergy analysis for identification and reduction of internal inefficiencies, and (3) techno-economic evaluation to confirm overall energy performance. Three heat exchanger network layouts with distinct HTHP operating temperatures were modeled, achieving total COP values of 2.20, 2.29, and 2.14, with corresponding second law efficiencies of 45.1 %, 46.8 %, and 43.8 %. A key insight is that maximizing the COP of individual HTHPs does not ensure the highest overall process efficiency when multiple HTHPs are integrated. The parametric study evaluates the influence of process conditions, supplemental electric boiler capacity, and energy prices. Implementing process modifications instead of relying on fixed process parameters reduces the specific power consumption by 15 %, from 0.415 kWh/kg to 0.354 kWh/kg.

This study presents the development and experimental validation of a dynamic simulation model for a transcritical CO₂ heat pump system coupled with a stratified water tank, with particular focus on strong transient behavior and detailed heat exchanger characteristics. Due to the unique thermophysical properties of CO₂ under transcritical conditions, conventional modeling approaches are insufficient. The model was validated against experimental results under a range of operating conditions. It accurately predicted outlet water temperatures within ±3.2 °C and system COP within ±6.8% deviation from measurements. In contrast to previous models, this approach offers improved accuracy in capturing dynamic system responses, including startup transients, and demonstrates high adaptability across varying ambient temperatures and load profiles. Importantly, the model also considers the vertical installation layout of components, enabling analysis of gravitational effects on system dynamics and offering insights into optimal configuration strategies. The validated model serves as a powerful tool for system optimization and advanced control design in residential CO₂ heat pump applications.

Modeling ionic activity is essential for understanding ion-specific effects in environmental and industrial systems. The microscopic interactions of heavy metal salts in aqueous solutions become increasingly complex with rising salt molality, posing significant challenges for accurately modeling ionic activity. This study applies, for the first time, an electrolyte version of the cubic-plus-association equation of state to calculate the mean ionic activity coefficients of several heavy metal salts in water at 298.15 K. Experimental data for four types of salts—nickel, cadmium, cobalt, and zinc salts—were compiled, and the ion–water binary interaction parameters are regressed from these measurements. The model successfully predicts water activity, osmotic coefficients, and mean ionic activity coefficients across most systems. Without explicit incorporation of ionic association, the average calculation relative average deviation of the mean ionic activity coefficient for nickel and cobalt salts is 5.9% and 6.3%, respectively, extending the applicable molality range up to 5.0 mol/kg water and 5.5 mol/kg water. For solutions of cadmium salts and zinc salts that have been confirmed to contain ion pairs, ion association was introduced into the model for calculation. The calculation results indicate that the consideration of ion association has significantly reduced the calculation deviations. Within the calculated salt molality ranges, the average relative deviation of the mean ionic activity coefficient for cadmium salts is all below 2.0%, while that for zinc salts is all less than 5.5%, respectively. This study provides valuable insights into the micro-level factors influencing activity coefficients and offers recommendations for improving model accuracy under complex conditions.