Near-field radiative heat transfer between nanoporous GaN films

Photon tunneling effects give rise to surface waves,amplifying radiative heat transfer in the near-field regime.Recent research has highlighted that the introduction of nanopores into materials creates additional pathways for heat transfer,leading to a substantial enhancement of near-field radiative heat transfer(NFRHT).Being a direct bandgap semiconductor,GaN has high thermal conductivity and stable resistance at high temperatures,and holds significant potential for applications in optoelectronic devices.Indeed,study of NFRHT between nanoporous GaN films is currently lacking,hence the physical mechanism for adding nanopores to GaN films remains to be discussed in the field of NFRHT.In this work,we delve into the NFRHT of GaN nanoporous films in terms of gap distance,GaN film thickness and the vacuum filling ratio.The results demonstrate a 27.2%increase in heat flux for a 10 nm gap when the nanoporous filling ratio is 0.5.Moreover,the spectral heat flux exhibits redshift with increase in the vacuum filling ratio.To be more precise,the peak of spectral heat flux moves fromω=1.31×10^(14)rad·s^(-1)toω=1.23×10^(14)rad·s^(-1)when the vacuum filling ratio changes from f=0.1 to f=0.5;this can be attributed to the excitation of surface phonon polaritons.The introduction of graphene into these configurations can highly enhance the NFRHT,and the spectral heat flux exhibits a blueshift with increase in the vacuum filling ratio,which can be explained by the excitation of surface plasmon polaritons.These findings offer theoretical insights that can guide the extensive utilization of porous structures in thermal control,management and thermal modulation.

Arranging cation mixing and charge compensation of TiNb_(2)O_(7) with W^(6+) doping for high lithium storage performance

TiNb_(2)O_(7) is an advanced anode material for high-energy density lithium-ion batteries(LIBs) due to its considerable specific capacity and satisfactory safety.However,its rate capability is limited by its poor ionic conductivity and electronic conductivity.To solve this problem,TiNb_(2)O_(7) with W^(6+) doping was synthesized by a convenient solid-state method.The doping of W^(6+) will lead to arranging cation mixing and charge compensation.The cation rearrangement creates a new Li-conductive environment for lithiation,resulting in a low-energy barrier and the fast Li^(+)storage/diffusion.The results show that the Li^(+)diffusion coefficient of W_(0.06)Ti_(0.91)Nb_(2)O_(7) is increased by 9.96 times greater than that of TiNb_(2)O_(7).Besides,as the calculation proves,due to the partial reduction of the Nb^(5+)and Ti^(4+) caused by charge compensation,W^(6+)doping results in low charge transfer resistance and excellent electronic conductivity.Moreover,W^(6+) doping accounts for a high pseudocapacitive contribution.At the scan rate of 1 mV·s^(-1),the pseudocapacitive contribution for TiNb_(2)O_(7) is 78%,while that for W_(0.06)Ti_(0.91)Nb_(2)O_(7) increases to 83%.The reversible specific capacity of W_(0.06)Ti_(0.91)Nb_(2)O_(7) after 600 cycles is maintained at 148.90mAh·g^(-1) with a loss of only 16.37% at 10.0C.Also,it delivers a commendable capacity of 161.99 mAh·g^(-1) at20.0C.Even at 30.0C,it still retains a satisfactory capacity of 147.22 mAh·g^(-1),much higher than TiNb_(2)O_(7)(97.49mAh·g^(-1)).Our present study provides ideas for the development of electrode materials for lithium-ion batteries.

Atomic-scale simulations in multi-component alloys and compounds:A review on advances in interatomic potential

Multi-component alloys have demonstrated excellent performance in various applications,but the vast range of possible compositions and microstructures makes it challenging to identify optimized alloys for specific purposes.To overcome this challenge,large-scale atomic simulation techniques have been widely used for the design and optimization of multi-component alloys.The capability and reliability of large-scale atomic simulations essentially rely on the quality of interatomic potentials that describe the interactions between atoms.This work provides a comprehensive summary of the latest advances in atomic simulation techniques for multi-component alloys.The focus is on interatomic potentials,including both conventional empirical potentials and newly developed machine learning potentials(MLPs).The fitting processes for different types of interatomic potentials applied to multi-component alloys are also discussed.Finally,the challenges and future perspectives in developing MLPs are thoroughly addressed.Overall,this review provides a valuable resource for researchers interested in developing optimized multicomponent alloys using atomic simulation techniques.