Jungwoo Shin, Sanghyeon Kim, Hoonkee Park, Ho Won Jang, David G. Cahill, Paul V. Braun
Upon insertion and extraction of lithium, materials important for electrochemical energy storage can undergo changes in thermal conductivity ($Λ$) and elastic modulus ($\it M$). These changes are attributed to evolution of the intrinsic thermal carrier lifetime and interatomic bonding strength associated with structural transitions of electrode materials with varying degrees of reversibility. Using in situ time-domain thermoreflectance (TDTR) and picosecond acoustics, we systemically study $Λ$ and $\it M$ of conversion, intercalation and alloying electrode materials during cycling. The intercalation V$_{2}$O$_{5}$ and TiO$_{2}$ exhibit non-monotonic reversible $Λ$ and $\it M$ switching up to a factor of 1.8 ($Λ$) and 1.5 ($\it M$) as a function of lithium content. The conversion Fe$_{2}$O$_{3}$ and NiO undergo irreversible decays in $Λ$ and $\it M$ upon the first lithiation. The alloying Sb shows the largest and partially reversible order of the magnitude switching in $Λ$ between the delithiated (18 W m$^{-1}$ K$^{-1}$) and lithiated states (<1 W m$^{-1}$ K$^{-1}$). The irreversible $Λ$ is attributed to structural degradation and pulverization resulting from substantial volume changes during cycling. These findings provide new understandings of the thermal and mechanical property evolution of electrode materials during cycling of importance for battery design, and also point to pathways for forming materials with thermally switchable properties.
HyunJin Kim, Jungwoo Shin, Alberto A. Del Barrio
This paper proposes a training method having multiple cyclic training for achieving enhanced performance in low-bit quantized convolutional neural networks (CNNs). Quantization is a popular method for obtaining lightweight CNNs, where the initialization with a pretrained model is widely used to overcome degraded performance in low-resolution quantization. However, large quantization errors between real values and their low-bit quantized ones cause difficulties in achieving acceptable performance for complex networks and large datasets. The proposed training method softly delivers the knowledge of pretrained models to low-bit quantized models in multiple quantization steps. In each quantization step, the trained weights of a model are used to initialize the weights of the next model with the quantization bit depth reduced by one. With small change of the quantization bit depth, the performance gap can be bridged, thus providing better weight initialization. In cyclic training, after training a low-bit quantized model, its trained weights are used in the initialization of its accurate model to be trained. By using better training ability of the accurate model in an iterative manner, the proposed method can produce enhanced trained weights for the low-bit quantized model in each cycle. Notably, the training method can advance Top-1 and Top-5 accuracies of the binarized ResNet-18 on the ImageNet dataset by 5.80% and 6.85%, respectively.
Xin Qian, Jungwoo Shin, Yaodong Tu, James Han Zhang, Gang Chen
Harvesting waste heat with temperatures lower than 100 oC can improve system efficiency and reduce greenhouse gas emissions, yet it has been a longstanding and challenging task. Electrochemical methods for harvesting low-grade heat have attracted research interest in recent years due to the relatively high effective temperature coefficient of the electrolytes (> 1 mV/K) compared with the thermopower of traditional thermoelectric devices. Comparing with other electrochemical devices such as temperature-variation based thermally regenerative electrochemical cycle and temperature-difference based thermogalvanic cells, the thermally regenerative flow battery (TRFB) has the advantages of providing a continuous power output, decoupling the heat source and heat sink and recuperating heat, and compatible with stacking for scaling up. However, TRFB suffers from the issue of stable operation due to the challenge of pH matching between catholyte and anolyte solutions with desirable temperature coefficients. In this work, we demonstrate a PH-neutral TRFB based on KI/KI3 and K3Fe(CN)6/K4Fe(CN)6 as the catholyte and anolyte, respectively, with a cell temperature coefficient of 1.9 mV/K and a power density of 9 uW/cm2. This work also presents a comprehensive model with a coupled analysis of mass transfer and reaction kinetics in a porous electrode that can accurately capture the flow rate dependence of power density and energy conversion efficiency. We estimate that the efficiency of the pH-neutral TRFB can reach 11% of the Carnot efficiency at the maximum power output with a temperature difference of 37 K. Via analysis, we identify that the mass transfer overpotential inside the porous electrode and the resistance of the ion exchange membrane are the two major factors limiting the efficiency and power density, pointing to directions for future improvements.