A membrane-less desalination battery with ultrahigh energy efficiency

Dielectric capacitors with high energy storage performance are highly desired for advanced power electronic devices and systems. Even though strenuous efforts have been dedicated to closing the gap of energy storage density between the dielectric capacitors and the electrochemical capacitors/batteries, a single-minded pursuit of high energy density without a near-zero energy loss for ultrahigh energy efficiency as the grantee is in vain. Herein, for the purpose of decoupling the inherent conflicts between high polarization and low electric hysteresis (loss), and achieving high energy storage density and efficiency simultaneously in multilayer ceramic capacitors (MLCCs), we propose an interlaminar strain engineering strategy to modulate the domain structure and manipulate the polarization behavior of the dielectric mediums. With a heterogeneous layered structure consisting of different antiferroelectric ceramics [(Pb0.9Ba0.04La0.04)(Zr0.65Sn0.3Ti0.05)O3/(Pb0.95Ba0.02La0.02)(Zr0.6Sn0.4)O3/(Pb0.92Ca0.06La0.02)(Zr0.6Sn0.4)0.995O3], our MLCC exhibits a giant recoverable energy density of 22.0 J cm−3 with an ultrahigh energy efficiency of 96.1%. Combined with the favorable temperature and frequency stabilities and the high antifatigue property, this work provides a strain engineering paradigm for designing MLCCs for high-power energy storage and conversion systems.

Developing advanced solar-driven interfacial evaporators with both ultrahigh energy efficiency and long-term tolerability is highly desired but still a great challenge. Herein, inspired by the natural lotus, we develop a high-performance solar interfacial evaporator with a novel 3D biomimetic architecture. The lotus-inspired biomimetic evaporator (LBE) combines three key components, including a large "leaf" having strong solar energy absorption ability, hydrophilic "stems" working as water transport channels, and lotus root-like porous "roots" with minimized heat loss for improved respiration. The photothermal part in the LBE, analogous to a lotus leaf, possesses Janus wettability with a hydrophobic side above and a hydrophilic side below, which is achieved by a scalable method of in situ inducing ZIF-67 nanocubes into an electrospun fiber film followed by pyrolysis. In particular, the top side has a unique hierarchical network structure consisting of long porous carbon nanofibers with internally dispersed metal oxide nanocrystals, leading to highly efficient solar absorption of 91.37%. The 3D-LBE exhibits an extremely high evaporation rate of 3.23 kg m-2 h-1 and energy efficiency reaching 153.20% under 1-sun, which exceeds the theoretical limit and is the highest recorded, to the best of our knowledge. Notably, the 3D-LBE also shows impressive pollutant removal capabilities assuring long-term interfacial evaporation stability. The high-performance LBE promises many applications, such as wastewater treatment, sea salt production, and metal recovery.

Novel lead-free KNN-based ceramic with giant energy storage density, ultra-high efficiency and excellent thermal stability via relaxor strategy

Achieving high energy storage performance and ultrafast discharge speed in SrTiO3-based ceramics via a synergistic effect of chemical modification and defect chemistry

We have proposed and experimentally demonstrated a novel multi-bit content addressable memory (CAM) cell based on two series connected ferroelectric FETs (FeFETs). Thanks to the multilevel cell (MLC) operations of FeFETs and complementary inputs of search lines (SLs), CAM functions with 2-bits/cell are realized. Bit density can be further boosted by implementing more stable states of FeFETs. In addition, the CAM cells can be integrated in a form of 3D vertical FeFET NAND, which enables ultra-high density and energy efficiency at low cost. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> 4 FeFET NAND array is fabricated, and parallel search of a query with 4-bits data in a whole NAND block is experimentally demonstrated. The proposed CAM design is promising for future data-centric computing systems.

Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and performance for hardware implementations of deep neural networks (DNNs). An algorithm-hardware co-optimization framework is developed, which is applicable to different DNN types, sizes, and application scenarios. The algorithm part adopts the general block-circulant matrices to achieve a fine-grained tradeoff of accuracy and compression ratio. It applies to both fully-connected and convolutional layers and contains a mathematically rigorous proof of the effectiveness of the method. The proposed algorithm reduces computational complexity per layer from O(n2) to O(n log n) and storage complexity from O(n2) to O(n), both for training and inference. The hardware part consists of highly efficient Field Programmable Gate Array (FPGA)-based implementations using effective reconfiguration, batch processing, deep pipelining, resource re-using, and hierarchical control. Experimental results demonstrate that the proposed framework achieves at least 152X speedup and 71X energy efficiency gain compared with IBM TrueNorth processor under the same test accuracy. It achieves at least 31X energy efficiency gain compared with the reference FPGA-based work.

A recent trend in science has been focused on improving the energy efficiency of electrochemical and other energy conversion devices. This work continues the theme by reviewing the prospects for developing viable low-temperature fuel cells (LTFCs) that utilize hydrogen and oxygen and operate with exceedingly high energy efficiencies of 75–85%. The best currently available commercial LTFCs, for example, the polymer electrolyte membrane fuel cells (PEMFCs) used in hydrogen hybrid vehicles, typically operate at energy efficiencies of ≤60%, generating ≤20.0 kWh of electricity per 1 kg of hydrogen consumed. When combined with state-of-the-art commercial water electrolyzers, which require ∼50 kWh to produce 1 kg of hydrogen, these systems offer a round-trip efficiency of only ∼40%. This makes them uncompetitive for large-scale energy storage. By contrast, future LTFCs operating at 75–85% energy efficiency would yield 25.0–28.3 kWh per 1 kg of hydrogen. When combined with recently developed water electrolyzers that require only ∼38.0–41.5 kWh to produce 1 kg of hydrogen, such systems could achieve round-trip efficiencies of up to 75%. This would be competitive with large-scale energy storage systems like pumped hydro, which provide a necessary alternative to batteries. Although Li-ion batteries can achieve round-trip efficiencies of ∼90%, their high cost and limited storage capacity hinder their use in grid-scale applications. Developing ultrahigh energy efficiency fuel cells therefore offers substantial promise as an alternative energy storage system. Achieving 75–85% efficiency at 80 °C requires fuel cells to operate at voltages of 0.9–1.0 V. This Review explores recent advances in electrocatalysts, interelectrode membranes, and other developments that have enabled PEMFCs, anion exchange membrane fuel cells (AEMFCs), and alkaline fuel cells (AFCs) that produce notable current densities at cell voltages of ≥0.9 V. The prospects of extending such technical achievements to the creation of fuel cells capable of viably operating at 75–85% energy efficiency are discussed. The most promising pathway to such ultrahigh energy efficiency is found to involve pressurizing the fuel cell gases to pressures notably higher than those used in commercial fuel cells at present (e.g., 4–10 bar).

Multilayer ceramic capacitors (MLCCs) play a crucial role in pulsed power applications because of their rapid charge/discharge capabilities. However, the combination of high energy density and high efficiency is the main challenge in practical applications. This study presents barium titanate-based (BaTiO₃-) lead-free relaxor ferroelectric (RFE) MLCCs formulated with 0.84BaTiO₃–0.16Bi(Mg_0.2Ni_0.2Zn_0.2Zr_0.2Nb_0.2)O₃ (0.84BT–0.16BMNZZN) and platinum inner electrodes via a tape-casting method. The introduction of the high-entropy component BMNZZN effectively enhances the relaxation behavior and local nanodomains while promoting grain refinement, resulting in a comprehensive improvement in insulation performance and energy storage performance. As a result, MLCCs exhibit excellent recoverable energy density (<i>W</i>_rec = 15.7 J∙cm⁻³) and ultrahigh efficiency (<i>η</i>) of 96.4% (@1614 kV∙cm⁻¹), simultaneously showing good temperature stability over a range of −120‒100 °C (<i>W</i>_rec ≈ 8.9 J∙cm⁻³ with a variation of less than ±4.85%, @1078 kV∙cm⁻¹) and excellent fatigue resistance (<i>W</i>_rec ≈ 9.2 J∙cm⁻³ with a variation of less than ±0.82% over 10⁷ cycles, and <i>η</i> greater than 95%, @1078 kV∙cm⁻¹). These findings indicate that BT–BMNZZN RFE MLCCs offer a viable solution for high-power energy storage capacitors.

Ultrahigh energy storage density and efficiency in A/B-site co-modified silver niobate relaxor antiferroelectric ceramics

Combining high energy efficiency and fast charge-discharge capability in novel BaTiO3-based relaxor ferroelectric ceramic for energy-storage

As the Convolutional Neural Network (CNN) goes deeper and more complex, the network becomes memory-intensive and computation-intensive. To address this issue, the lightweight neural network reduces parameters and Multiplication-and-Accumulation (MAC) operations by using the Depthwise Separable Convolution (DSC) to improve speed and efficiency. Nonetheless, the energy efficiency of classical Von Neumann architectures for CNNs is limited due to the memory wall challenge. Spin-based architectures have the potential to address this challenge thanks to the integration of memory and computing with ultra-high energy efficiency. However, deploying the DSC on spin-based architectures with the traditional dataflow leads to huge activation movements and low hardware utilization. Moreover, the inter-layer data dependency of neural networks increases latency. These factors become the bottleneck of improving energy efficiency and performance.Inspired by these challenges, we propose a novel dataflow on Spin-based Architectures for Lightweight neural networks (SAL). The novel dataflow replaces convolution unrolling by selecting activations in the crossbar according to the convolution window and also realizes the inter-layer data reuse. Moreover, the novel dataflow also reduces the latency due to the data dependency between layers, realizing higher performance. To the best of our knowledge, this is the first design to use hybrid dataflow for the PIM architecture. We also optimize the structure of the spin-based crossbar and the pipeline based on the dataflow to achieve better data reuse and computational parallelism. For deploying the MobileNet V1, the novel dataflow improves the hardware utilization by 23×∼ 105× and reduces the data traffic by 1.09×∼ 18.6×. Compared with the NEBULA, a spin-based non-Von Neumann architecture, the SAL reduces the energy consumption by 4× and improves the performance by 7.3×, which are 0.32mJand 10.43GOPs-1, respectively. Moreover, the SAL improves power efficiency over 29 times more than the NEBULA. Compared with the Eyeriss, the SAL improves the energy efficiency by four orders of magnitude.

Achieved ultrahigh energy storage properties and outstanding charge–discharge performances in (Na0.5Bi0.5)0.7Sr0.3TiO3-based ceramics by introducing a linear additive

Harnessing natural resources (solar, seawater) to explore next-generation sustainable energy storage is an intriguing yet challenging task. Here, we establish a unique floating photoelectric/photothermal dual-activated catalytic platform based on engineered Janus aerogel that overcomes inherent mass transport/kinetics constraints in neutral triphase oxygen reaction systems to enable a full-spectrum-responsive seawater battery with ultrahigh energy efficiency. Our approach employs in situ region-selective assembly of the rationally designed fluorinated donor-acceptor polymers on carbon-nanotube aerogel (FMTAPPC), creating a new floatable FMTAPPC photoelectrode with asymmetric wettability, superior photoelectric/photothermal dual-response, and bifunctional oxygen evolution/reduction (OER/ORR) catalysis. This all-in-one integration enables floating FMTAPPC to maximize sunlight utilization for interface micro-environment regulation and adsorbate binding optimization via photoelectric/photothermal dual-activation while satisfying paradoxical wetting requirements of OER/ORR, which tunes reaction kinetics and intermediate energetics to remarkably promote both OER/ORR in neutral media. Under sunlight, floating FMTAPPC delivers large current densities for both OER/ORR-3-4 fold-enhancement over dark conditions. This enables the seawater-based Zn-air battery to simultaneously improve charge/discharge performance at high rates and deliver ultrahigh round-trip efficiency of 128.2%-surpassing most photo-assisted batteries. Mechanistic studies unveil that photoelectric/photothermal effects synergistically boost OER/ORR kinetics, while the photoelectric effect dominates intermediate energetics optimization, substantially lowering reaction energy barriers.

Bio-derived ultrathin membrane for solar driven water purification

Dielectric ceramics with high energy storage performance are crucial for the development of advanced high-power capacitors. However, achieving ultrahigh recoverable energy storage density and efficiency remains challenging, limiting the progress of leading-edge energy storage applications. In this study, (Bi1/2Na1/2)TiO3 (BNT) is selected as the matrix, and the effects of different A-site elements on domain morphology, lattice polarization, and dielectric and ferroelectric properties are systematically investigated. Mg, La, Ca, and Sr are shown to enhance relaxation behavior by different magnitudes; hence, a high-entropy strategy for designing local polymorphic distortions is proposed. Based on atomic-scale investigations, a series of BNT-based high-entropy compositions are designed by introducing trace amounts of Mg and La to improve the electric breakdown strength and further disrupt the polar nanoscale regions (PNRs). A disordered polarization distribution and ultrasmall PNRs with a minimum size of ≈1 nm are detected in the high-entropy ceramics. Ultimately, a high recoverable energy density of 10.1 Jcm-3 and an efficiency of 90% are achieved for (Ca0.2Sr0.2Ba0.2Mg0.05La0.05Bi0.15Na0.15)TiO3. Furthermore, it displays a high-power density of 584 MWcm-3 and an ultrashort discharge time of 27 ns. This work presents an effective approach for designing dielectric energy storage materials with superior comprehensive performance via a high-entropy strategy.