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1. Thermodynamically-informed Air-based Soft Heat Engine Design

   Charles Xiao, Luke F. (January 2021, ArXivorg)

   null (Ed.)

   Soft heat engines are poised to play a vital role in future soft robots due to their easy integration into soft structures and low-voltage power requirements. Recent works have demonstrated soft heat engines relying on liquid-to-gas phase change materials. However, despite the fact that many soft robots have air as a primary component, soft air cycles are not a focus of the field. In this paper, we develop theory for air-based soft heat engines design and efficiency, and demonstrate experimentally that efficiency can be improved through careful cycle design. We compare a simple constant-load cycle to a designed decreasing-load cycle, inspired by the Otto cycle. While both efficiencies are relatively low, the Otto-like cycle improves efficiency by a factor of 11.3, demonstrating the promise of this approach. Our results lay the foundation for the development of air-based soft heat engines as a new option for powering soft robots. 

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2. Efficiency of Harmonic Quantum Otto Engines at Maximal Power

   https://doi.org/10.3390/e20110875  

   Deffner, Sebastian (November 2018, Entropy)

   Recent experimental breakthroughs produced the first nano heat engines that have the potential to harness quantum resources. An instrumental question is how their performance measures up against the efficiency of classical engines. For single ion engines undergoing quantum Otto cycles it has been found that the efficiency at maximal power is given by the Curzon–Ahlborn efficiency. This is rather remarkable as the Curzon–Alhbron efficiency was originally derived for endoreversible Carnot cycles. Here, we analyze two examples of endoreversible Otto engines within the same conceptual framework as Curzon and Ahlborn’s original treatment. We find that for endoreversible Otto cycles in classical harmonic oscillators the efficiency at maximal power is, indeed, given by the Curzon–Ahlborn efficiency. However, we also find that the efficiency of Otto engines made of quantum harmonic oscillators is significantly larger. 

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3. Boosting engine performance with Bose–Einstein condensation

   https://doi.org/10.1088/1367-2630/ac47cc  

   Myers, Nathan M.; Peña, Francisco J.; Negrete, Oscar; Vargas, Patricio; De Chiara, Gabriele; Deffner, Sebastian (February 2022, New Journal of Physics)

   Abstract At low-temperatures a gas of bosons will undergo a phase transition into a quantum state of matter known as a Bose–Einstein condensate (BEC), in which a large fraction of the particles will occupy the ground state simultaneously. Here we explore the performance of an endoreversible Otto cycle operating with a harmonically confined Bose gas as the working medium. We analyze the engine operation in three regimes, with the working medium in the BEC phase, in the gas phase, and driven across the BEC transition during each cycle. We find that the unique properties of the BEC phase allow for enhanced engine performance, including increased power output and higher efficiency at maximum power. 

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4. Protective Spinel Coating for Li1.17Ni0.17Mn0.50Co0.17O2 Cathode for Li-Ion Batteries through Single-Source Precursor Approach

   https://doi.org/10.3390/nano10091870  

   Shevtsov, Andrey; Han, Haixiang; Morozov, Anatolii; Carozza, Jesse C.; Savina, Aleksandra A.; Shakhova, Iaroslava; Khasanova, Nellie R.; Antipov, Evgeny V.; Dikarev, Evgeny V.; Abakumov, Artem M. (September 2020, Nanomaterials)

   null (Ed.)

   The Li1.17Ni0.17Mn0.50Co0.17O2 Li-rich NMC positive electrode (cathode) for lithium-ion batteries has been coated with nanocrystals of the LiMn1.5Co0.5O4 high-voltage spinel cathode material. The coating was applied through a single-source precursor approach by a deposition of the molecular precursor LiMn1.5Co0.5(thd)5 (thd = 2,2,6,6-tetramethyl-3,5-heptanedionate) dissolved in diethyl ether, followed by thermal decomposition at 400 °C inair resulting in a chemically homogeneous cubic spinel. The structure and chemical composition of the coatings, deposited on the model SiO2 spheres and Li-rich NMC crystallites, were analyzed using powder X-ray diffraction, electron diffraction, high angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), and energy-dispersive X-ray (EDX) mapping. The coated material containing 12 wt.% of spinel demonstrates a significantly improved first cycle Coulombic efficiency of 92% with a high first cycle discharge capacity of 290 mAhg−1. The coating also improves the capacity and voltage retention monitored over 25 galvanostatic charge–discharge cycles, although a complete suppression of the capacity and voltage fade is not achieved. 

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5. C3SRAM: An In-Memory-Computing SRAM Macro Based on Robust Capacitive Coupling Computing Mechanism

   https://doi.org/10.1109/JSSC.2020.2992886  

   Jiang, Zhewei; Yin, Shihui; Seo, Jae-sun; Seok, Mingoo (May 2020, IEEE Journal of Solid-State Circuits)

   This article presents C3SRAM, an in-memory-computing SRAM macro. The macro is an SRAM module with the circuits embedded in bitcells and peripherals to perform hardware acceleration for neural networks with binarized weights and activations. The macro utilizes analog-mixed-signal (AMS) capacitive-coupling computing to evaluate the main computations of binary neural networks, binary-multiply-and-accumulate operations. Without the need to access the stored weights by individual row, the macro asserts all its rows simultaneously and forms an analog voltage at the read bitline node through capacitive voltage division. With one analog-to-digital converter (ADC) per column, the macro realizes fully parallel vector–matrix multiplication in a single cycle. The network type that the macro supports and the computing mechanism it utilizes are determined by the robustness and error tolerance necessary in AMS computing. The C3SRAM macro is prototyped in a 65-nm CMOS. It demonstrates an energy efficiency of 672 TOPS/W and a speed of 1638 GOPS (20.2 TOPS/mm 2 ), achieving 3975 × better energy–delay product than the conventional digital baseline performing the same operation. The macro achieves 98.3% accuracy for MNIST and 85.5% for CIFAR-10, which is among the best in-memory computing works in terms of energy efficiency and inference accuracy tradeoff. 

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