Limited Footprint Research Articles

Improving Electrochemical Performance in Planar On-Chip Zn-ion Micro-Batteries via Interlayer Strategies.

The imperative development of planar on-chip micro-batteries featuring high-capacity electrodes and environmentally safer, cost-effective, and stable systems is crucial for powering forthcoming miniaturized systems-on-chip smart devices. However, research in the area of high-stability micro-batteries is limited due to the complex fabrication process, the stability of micro-electrodes during cycling, and the challenge of maintaining higher capacity within a limited device footprint. In response to this need, this study focuses on providing highly stable and high-capacity micro-electrodes. This involves adding a PEDOT layer between the electrode material and the current collector, applied within a planar polyaniline cathode and zinc anode device structure to enhance charge storage performance. This straightforward strategy not only improves device stability over long-term cycling and reduces charge transfer resistance but also increases charge storage capacities from 17.64 to 19.75 µAhcm- 2 at 0.1 mAcm- 2. Consequently, the Zn-ion micro-batteries achievenotable peak areal energy and power of 18.82 µWhcm- 2 and 4.37 mWcm- 2, respectively. This work proposes an effective strategy to enhance the electrochemical performance of planar micro-batteries, a critical advancement for the development of advanced portable electronics.

Miniaturized on-chip spectrometer enabled by electrochromic modulation.

Miniaturized on-chip spectrometers with small footprints, lightweight, and low cost are in great demand for portable optical sensing, lab-on-chip systems, and so on. Such miniaturized spectrometers are usually based on engineered spectral response units and then reconstruct unknown spectra with algorithms. However, due to the limited footprints of computational on-chip spectrometers, the recovered spectral resolution is limited by the number of integrated spectral response units/filters. Thus, it is challenging to improve the spectral resolution without increasing the number of used filters. Here we present a computational on-chip spectrometer using electrochromic filter-based computational spectral units that can be electrochemically modulated to increase the efficient sampling number for higher spectral resolution. These filters are directly integrated on top of the photodetector pixels, and the spectral modulation of the filters results from redox reactions during the dual injection of ions and electrons into the electrochromic material. We experimentally demonstrate that the spectral resolution of the proposed spectrometer can be effectively improved as the number of applied voltages increases. The average difference of the peak wavelengths between the reconstructed and the reference spectra decreases from 1.61 nm to 0.29 nm. We also demonstrate the proposed spectrometer can be worked with only four or two filter units, assisted by electrochromic modulation. In addition, we also demonstrate that the electrochromic filter can be easily adapted for hyperspectral imaging, due to its uniform transparency. This strategy suggests a new way to enhance the performance of miniaturized spectrometers with tunable spectral filters for high resolution, low-cost, and portable spectral sensing, and would also inspire the exploration of other stimulus responses such as photochromic and force-chromic, etc, on computational spectrometers.

Evolution of hydrochemical characteristics and the influence of environmental background in the Hailar River basin, China.

Understanding the evolution of hydrochemical characteristics in river systems is essential for environmental assessment and water resource management. This study explores the spatiotemporal distribution and the determinants of hydrochemical characteristics in the Hailar River basin, China, over an extensive period. Our results revealed that CODMn and CODCr were the primary concerns for long-term river management, with exceedance rates of 42.92% and 50.62%, respectively. These exceedances were predominantly driven by interactions between riparian soils and surface water, rather than anthropogenic pollution, as suggested by the strong correlations between dissolved organic carbon and soil water-extractable organic carbon, and the limited human footprint in this region. Piper trilinear and Gibbs diagram analysis further revealed that long-term rack weathering shaped the basin's hydrochemical characteristics, resulting in distinct HCO3--Ca2+ and HCO3--Ca2+-Na+ signatures. In addition, APCS-MLR analysis identified that elevated of CODMn and CODCr levels were mainly attributed to the interactions with adjacent soils, which are extensively covered by forests and grasslands. In contrast, leaching and migration processes contributed significantly on total dissolved solids and total phosphorus. The study also found that environmental self-purification processes played a key role in regulating Fe concentrations. This investigation provides a nuanced understanding of the environmental background's influence on hydrochemistry and dissolved organic matter (DOM) in the Hailar River basin, which offers valuable insights and methodologies for the rational assessment of water quality and aquatic ecosystem health in similar riverine systems.

NeRF-OR: neural radiance fields for operating room scene reconstruction from sparse-view RGB-D videos.

RGB-D cameras in the operating room (OR) provide synchronized views of complex surgical scenes. Assimilation of this multi-view data into a unified representation allows for downstream tasks such as object detection and tracking, pose estimation, and action recognition. Neural radiance fields (NeRFs) can provide continuous representations of complex scenes with limited memory footprint. However, existing NeRF methods perform poorly in real-world OR settings, where a small set of cameras capture the room from entirely different vantage points. In this work, we propose NeRF-OR, a method for 3D reconstruction of dynamic surgical scenes in the OR. Where other methods for sparse-view datasets use either time-of-flight sensor depth or dense depth estimated from color images, NeRF-OR uses a combination of both. The depth estimations mitigate the missing values that occur in sensor depth images due to reflective materials and object boundaries. We propose to supervise with surface normals calculated from the estimated depths, because these are largely scale invariant. We fit NeRF-OR to static surgical scenes in the 4D-OR dataset and show that its representations are geometrically accurate, where state of the art collapses to sub-optimal solutions. Compared to earlier work, NeRF-OR grasps fine scene details while training 30 faster. Additionally, NeRF-OR can capture whole-surgery videos while synthesizing views at intermediate time values with an average PSNR of 24.86dB. Last, we find that our approach has merit in sparse-view settings beyond those in the OR, by benchmarking on the NVS-RGBD dataset that contains as few as three training views. NeRF-OR synthesizes images with a PSNR of 26.72dB, a 1.7% improvement over state of the art. Our results show that NeRF-OR allows for novel view synthesis with videos captured by a small number of cameras with entirely different vantage points, which is the typical camera setting in the OR. Code is available via: github.com/Beerend/NeRF-OR .

Planar Zn-Ion Microcapacitors with High-Capacity Activated Carbon Anode and VO2 (B) Cathode.

The downsizing of microscale energy storage devices plays a crucial role in powering modern emerging devices. Therefore, the scientific focus on developing high-performance microdevices, balancing energy density and power density, becomes essential. In this context, we explore an advanced Microplotter technique to fabricate hybrid planar Zn-ion microcapacitors (ZIMCs) that exhibit dual charge storage characteristics, with an electrical double layer capacitor type activated carbon anode and a battery type VO2 (B) cathode, aiming to achieve energy density surpassing supercapacitors and power density exceeding batteries. Effective loading of VO2 (B) cathode electrode materials combined with activated carbon anode onto confined planar microelectrodes not only provides reversible Zn2+ storage performance but also mitigates dendrite formation. This not only results in superior charge storage performance, including areal energies of 2.34 μWh/cm2 (at 74.76 μW/cm2) and 0.94 μWh/cm2 (at 753.12 μW/cm2), exceeding performance of zinc nanoparticle anode and activated carbon cathode based ZIMCs, but also ensures stable capacity retention of 87% even after 1000 cycles and free from any unwanted dendrites. Consequently, this approach is directed toward the development of high-performance ZIMCs by exploring high-capacity materials for efficient utilization on microelectrodes and achieving maximum possible capacities within the constraints of the limited device footprint.

Consistent representation joint adaptive adjustment for incremental zero-shot learning

Zero-shot learning aims to recognize objects from novel concepts through the model trained on seen class data and assisted by the semantic descriptions. Though it breaks the serious reliance on training data, it still fails to deal with the sequential streaming data in the open world. In this paper, we focus on the incremental zero-shot learning (IZSL) where the seen data arrives in the form of task sequence. In each incremental task, we only have access to the seen data of current task. The IZSL methods aim to depict the characteristics of current seen classes and avoid forgetting the previous ones, in the meantime, learn to generalize to the unseen classes. We summarize the challenges in IZSL as semantic drift which is further divided into task-recency bias and seen-class bias. To solve these issues, we propose a novel IZSL method termed as CRAA. Specifically, CRAA constructs consistent representations with satisfactory discrimination and generalization for all the seen and unseen classes. Based on these representations, CRAA learns a prototype classifier with a novel adaptive adjustment strategy to alleviate the task-recency bias and seen-class bias. Note that CRAA only needs a limited memory footprint to store the fixed scale model, and meets the demands of both memory restriction and data security in industry. We have conducted extensive experiments to evaluate our method on three widely used datasets. The results prove our method is superior to all the compared methods with significant improvements. Code is available at: https://github.com/changniu54/CRAA-Master.

(Invited) Industrialization of PEM Electrolyzer Stack Platforms: Driving Lab-Scale Innovation to Multi-MW Implementation

The Hydrogen production plays important role as part of energy transition strategy in Germany as well as in visions and subsequent policy of European Green Deal activities. Schaeffler [1] is driving further technological development with strong financial backing of European (CHP) and German funding (H2Giga). PEM water electrolysis is regarded as powerful technology, bringing advantages of limited installation footprint, broad range of operating conditions, especially in terms of dynamic load range and featuring high efficiency between 75-90% (based on HHV) [2].Minimizing the Levelized Cost of Hydrogen demands reduction in CAPEX, OPEX, maintenance whilst extending lifetime to secure full Return of Investment [3]. In addition, production of green hydrogen needs to anticipate the intermittent and distributed nature of renewable energy sources whilst functioning in a decentralized energy distribution network. Enabling maximum uptime sets high demands on the stack performance requirements and its material specifications. Here we show examples of how component development and design iterations make a huge impact on the commercial viability of electrolysers in industrial applications.Efficiency is key when converting renewable electricity and water into green hydrogen and other chemicals, even though PEM electrolyser stack technology is deemed capable of increasing current densities up to 10 A/cm2. Therefore, State-of-Art commercial stacks are rated at current densities between ~1-3 A/cm2, but this anticipated trend to increase power density underlines the necessity to reduce the total electrical resistance and avoid Ohm’s losses.Critical to success is selecting materials that can sustain functionality, stability and compatibility within an operational window being pushed to higher current density to increase stack output, whilst reducing PGM content and overall cost. Progress has been achieved by combining formed metal components with durable, conductive coatings, and establishing optimum compression.Most promising results published in open literature are based on small cells on laboratory scale (5-25 cm2 active area). So-called “screeners” are ideally suited for studying phenomena under well-controlled conditions with a plethora of diagnostic tools. In contrast, large-area stacks operated in the field don’t always operate under ideal operating conditions and can show variability in performance characteristics that escalate into unexpected degradation issues. Of course, stack behavior is dependent on its local environment within the BOP system, relative to its peers. We show how continuous remote monitoring during operation helps identify non-uniform stack operation and flag off-spec conditions, which could form the basis of Accelerated Stress Tests for testing in the laboratory.In this work we estimate how much stack technology can be improved further by forming BPP metal structures and optimizing the component design using CFD. Especially when scaling stack hardware to larger capacity, there is much emphasis on enhanced thermal management to avoid hot-spots and mitigate non-uniform degradation mechanisms and premature stack failure. Simulation results will show how flow designs could be adapted maintain uniform conditions when scaling up the active area or adding more cells to the stack.Robust Stacks are demanded for market ramp-up, demonstrating predictable performance over a long lifetime and insensitivity to a wide range of possible operating conditions. However, but to the best of our knowledge, no component- nor stack- supplier is willing to provide extensive guarantees. Fortunately, stacks quality can be improved significantly by strong automotive and manufacturing industries stepping into the PEM electrolyzer arena and introducing their automated assembly, mature manufacturing and controlled processes to guarantee reproducibility, tight tolerances, superior quality and support supply chain development. Before ramping up global production capacity massively, all players would benefit from standardizing general geometric specifications of stacks so we can leverage a stronger collective learning curve with increasing numbers.

Environmental impact assessment of dicationic ionic liquids with ammonium-phosphonium cations and amino acid anions

Progress in the development of biodegradable or biobased ionic liquids (ILs) has led to the design of green compounds for several applications. Herein, four biocompatible dicationic ionic liquids (DILs) with ammonium-phosphonium cations and amino acid anions were synthesized and investigated their environmental impact. The structures of the DILs were confirmed by spectral analyses (1H, 13C and 31P NMR). Furthermore, physicochemical properties such as density, viscosity and refractive index were determined. Water content, bromide content and solubility were thereafter determined as the parameters needed for further studies. Subsequently, their antifeedant activity towards economically important pests of grain in storage warehouses: the granary weevil, the confused flour beetle, and the khapra beetle was examined, showing the dependence on structure. Moreover, selected DILs were investigated for toxicity towards white mustard, Daphnia magna, and Artemia franciscana to specify the environmental impact. These studies were complemented by understand the biodegradation of DILs by bacterial communities derived from soil at the agricultural land. The result was DILs with limited environmental footprints that have great potential for further application studies.

Influence of canopy structural complexity on urban woodland butterfly species richness

AbstractAs urban land use expands, understanding the conservation of biodiversity in urban settings becomes increasingly important. Urban areas contain a wide range of remnant natural, semi-natural and anthropogenic woodlands but the biodiversity of these woodlands is not well studied. Urban woodlands are usually constrained in size and subject to management which may not be focused on promoting biodiversity. As a habitat of critical importance, the characteristics of urban woodlands need to be better understood to maximise their biodiversity within a limited footprint in the urban landscape. We used repeat line-transect surveys and rapid woodland structure habitat assessments to investigate the habitat associations of butterflies in ten woodland patches representing a range of sizes (between 1 and 40 ha), ages and woodland characteristics in the urban habitat matrix of Milton Keynes, UK. We found that ancient woodland sites supported every species detected, but the butterfly communities of amenity and roadside woodlands had similar species richness, diversity and abundance, regardless of size. Butterfly species richness was strongly associated with both woodland area and structural complexity at a site-scale, but only with structural complexity on a transect scale. Simpson diversity showed no correlation with any variable at the site scale, but strong correlation with structural complexity on transects. Abundance only correlated with area at the site scale. Our results suggest that management techniques that introduce structural complexity within urban woodlands may be an effective way to support butterfly richness and diversity in contexts where woodland areas cannot be increased.

Design of radiative cooling covers for automobiles with maximized cooling power

Due to the capability of cooling objects without energy consumption by emitting thermal radiation into outer space, radiative cooling technologies have found broad applications in different scenarios. Despite the high demand for cooling automobiles under sunlight, there is no specialized design of a radiative cooling cover for automobiles. This paper demonstrates the first specialized radiative cooling cover for automobiles. As the overall cooling power is linearly proportional to the surface area, we designed a conch-shaped foldable radiative cooling film car cover to maximize the surface area within a limited space, resulting in a total cooling power of 5,726.8 W. As a result, the maximum temperature drop can reach 27 °C under strong solar irradiance (800 Wm−2). In addition, we further demonstrate the effects of the surface coverage on the cooling performance by measuring the temperature drop at different surface coverages (100 %, 90 %, 40 %, and 10 %). It is found that the radiative cooling cover can effectively cool the inner temperature of a car even with a small surface coverage. In addition, it confirms that enlarging surface area is an effective way to improve cooling performance. Our results open new possibilities in designing radiative cooling covers that can achieve large cooling power within a limited footprint. The design principle can be extended and generally applied to other radiative cooling devices for various applications.

Penta-band polarization insensitive ultra-thin metamaterial absorber suitable for flux shielding in WPT

This study introduces a novel multi-band, polarization-insensitive metamaterial absorber, designed by combining a dual-band and a triple-band resonator structures. Through the amalgamation of discrete absorption responses from these structures, the resulting design exhibits five absorption peaks at frequencies: 1.5 GHz (80%), 3.6 GHz (88%), 5.46 GHz (95%), 7.17 GHz (95%), and 9.17 GHz (95%). One of the standout features of the proposed design lies in its four-fold symmetry, endowing it with polarization insensitivity. This ensures robust performance regardless of the incident polarization, enhancing the versatility and applicability of the absorber in various scenarios. The design’s compact nature is noteworthy, with a unit cell dimension of only 0.125λ. This compactness not only contributes to space-efficient deployment but also underscores the efficiency of the metamaterial in achieving multiple absorption peaks within a limited spatial footprint. In addition to its compactness, the absorber is ultra-thin, with a dielectric thickness of merely 0.008λ. This characteristic is particularly advantageous for applications where a low-profile and lightweight solution is essential, such as in the development of inconspicuous communication devices or electromagnetic shielding.

A Miniatured Fully Integrated High Resolution and Accuracy Capacitive Angle Encoder

In this paper, a capacitive angle encoder monitored and sensed by ASIC with a sensitive structure fabricated by MEMS technology is proposed. The sensitive structure with a 27.5mm outer diameter is designed and optimized in a limited footprint to realize higher accuracy. After being fabricated by MEMS technology, it is integrated with an ASIC, realizing high fabrication accuracy and a small footprint at the same time. The ASIC is designed in an analog-digital hybrid architecture to achieve low power consumption as well as a small footprint. The analog part of the ASIC consists of a charge to voltage converter with capacitance cancellation array and a sigma-delta modulator (SDM) with a 3-bit quantizer. In the digital part of ASIC, the processor based on Cortex-M3 core is designed to perform angle calculation. The measurement result of the proposed angle sensor reveals that the power consumption is 250 mW from a single 5V supply, the bandwidth is over 130Hz, the resolution is 0.002° and the accuracy over the full range is 0.024°, indicating that the encoder has considerable potential for use in high-precision applications.

AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks

Exploring the expected quantizing scheme with suitable mixed-precision policy is the key point to compress deep neural networks (DNNs) in high efficiency and accuracy. This exploration implies heavy workloads for domain experts, and an automatic compression method is needed. However, the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios. In this paper, we propose an end-to-end framework named AutoQNN, for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor. AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques: quantizing scheme search (QSS), quantizing precision learning (QPL), and quantized architecture generation (QAG). QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search, and then uses the differentiable neural architecture search (DNAS) algorithm to seek the layer- or model-desired scheme from the set. QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes, to the best of our knowledge. QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint. QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention, to facilitate end-to-end neural network quantization. We have implemented AutoQNN and integrated it into Keras. Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization.

Site Selection for Wind Energy as an Alternative Source of Energy in Bonny, Nigeria

Power derived via the wind stands out as an appealing form of renewable energy due to its minimal operational, maintenance, and production expenses, coupled with its limited environmental footprint. This investigation focuses on employing geospatial methods to establish a wind farm on Bonny Island, Nigeria. The primary objectives include furnishing data and a spatial wind distribution map for Bonny Island, evaluating the significance of factors crucial for wind farm development in the area, and generating a wind energy suitability map. The study utilizes Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) methodologies to scrutinize five critical parameters influencing location suitability. The findings indicate that Bonny Island possesses potential for wind farm installation, with 3,549.8 hectares, 10,219.6 hectares, and 424.6 hectares categorized as highly suitable, suitable, and unsuitable, respectively. Moreover, wind speed, land use/land cover, distance from the road, distance from the river, and land slope each carry a substantial priority weight of 50%, 25%, 10%, 10%, and 5%, respectively. These weights contribute to the creation of a wind energy suitability map for the study area. This research recommends amongst other things the investment and installation of a wind energy farm in Bonny Island, owing to the comparative advantage over other sources of energy in Nigeria.

Fully automated dual-run manufacturing of [11C]PIB on FASTlab

This report describes an updated, fully automated method for the production of [11C]PIB on a cassette-based automated synthesis module. The method allows for two separate productions of [11C]PIB, both of which meet all specification for use in clinical studies. The GE FASTlab developer system was used to create the cassette design as well as the controlling tracer package. The method takes 16 min from the delivery of [11C]MeOTf to the FASTlab, or 35 min from the End of Bombardment; and reliably produces 3547 ± 586 MBq of [11C]PIB in high radiochemical purity (> 98 %). This methodology increases the production capacity of radiopharmaceutical facilities for [11C]PIB, and can easily produce 4 batches in a single day with limited infrastructure footprint.

From Academia to Industry: Criteria for Upscaling Ionic Liquid-Based Thermo-Electrochemical Cells for Large-Scale Applications

Thermo-electrochemical cells (or thermocells) represent a promising technology to convert waste heat energy into electrical energy, generating power with minimal material consumption and a limited carbon footprint. Recently, the adoption of ionic liquids has pushed both the operational temperature range and the power output of thermocells. This research discusses the design challenges and the key performance limitations that need to be addressed to deploy the thermocells in real-world applications. For this purpose, a unique up-scaled design of a thermocell is proposed, in which the materials are selected according to the techno-economic standpoint. Specifically, the electrolyte is composed of EMI-TFSI ionic liquid supplemented by [Co(ppy)]3+/2+ redox couples characterized by a positive Seebeck coefficient (1.5 mV/K), while the electrodes consist of carbon-based materials characterized by a high surface area. Such electrodes, adopted to increase the rate of the electrode reactions, lead to a thermoelectric performance one order of magnitude greater than the Pt electrode-based counterpart. However, the practical applications of thermocells are still limited by the low power density and low voltage that can be generated.

A 0.5 MP, 3D-Stacked, Voltage-Domain Global Shutter Image Sensor with NIR QE Enhancement, Event Detection Modes, and 90 dB Dynamic Range

We introduce a compact voltage-domain global shutter CMOS image sensor for a wide range of applications including consumer, IoT, and industrial applications. With 0.5 MP and 2.79 µm pixels packed in a die size of only 2.3 mm × 2.8 mm, the sensor achieves more than 92% quantum efficiency (QE) in the visible wavelength and more than 36% at near-infrared (940 nm) all while drawing a mere 20 mW at a 10-bit, 30-frame-per-second operational mode. In this article, we focus on the architecture of the sensor and the design challenges encountered to fit all the necessary circuitry in such a limited footprint. Moreover, we detail the new solutions we have developed to meet the demanding specifications of low-power operation and high dynamic range (HDR).

A compact SiGe D-band power amplifier for scalable photonic-enabled phased antenna arrays

To address the rising demand for high-speed wireless data links, communication systems operating at frequencies beyond {100},hbox {GHz} are being targeted. A key enabling technology in the development of these wireless systems is the phased antenna array. Yet, the design and implementation of such steerable antenna arrays at frequencies over {100},hbox {GHz} comes with a multitude of challenges. In particular, the cointegration of active electronics at each antenna element poses a major hurdle due to the inherent space constraints in the array. This article proposes a novel scalable concept for opto-electronic phased antenna arrays operating at 140 GHz. It details the system architecture of a transmitter that enables the implementation of large scale, wideband, 2D steerable phased antenna arrays and presents the design and measurement of a compact SiGe power amplifier (PA) chip to be used as one of its key building blocks. The amplifier achieves a gain of 20 dB at 135 GHz, features a P_{1dB} of 14.6 dBm and can support data rates up to 45 Gbps in a limited footprint of only 540μm × 550μm. This makes it one of the fastest, most powerful D-band power amplifiers in literature with a footprint compatible with frac{lambda }{2}-spaced phased array integration.

Rod-Shaped Linear Inertial Type Piezoelectric Actuator

This article presents a numerical and experimental investigation of a novel rod-shaped linear piezoelectric actuator that consists of a square cross-section-shaped rod with eight piezo ceramic plates and a cylindrical guidance rail. The rod has a hollow cut made with an offset from the longitudinal axis of the symmetry. A cylindrical guidance rail is placed on one side of the rod, while T-shaped clamping is formed on the opposite side. The slider is mounted on the rail and is moved along it. The actuator is compact, making it possible to mount it directly on a printed circuit board (PCB) or in another device with limited mounting space, restricted mass, or actuator footprint. The operation of the actuator is based on the excitation of the first longitudinal vibration mode of the rod that induces in-plane bending vibration of the nodal zone of the rod due to a hollowed cut asymmetrically placed in the central part of the actuator. The actuator is driven by two sawtooth waveform electric signals with the phase difference of π that allows exciting longitudinal deformations of the rod and controls the reverse motion of the slider. The results of numerical investigations confirmed the operation principle of the actuator at the frequency of 59.72 kHz. The maximum displacement amplitude of the guidance rail in the longitudinal direction reaches up to 152.9 μm while the voltage of 200 Vp-p was applied. An experimental investigation of the actuator was made, and a maximum linear speed of 45.6 mm/s and thrust force of 115.4 mN was achieved.

High Energy Density Porous RuO2 micro-Supercapacitors Using Protic Ionic Liquid Electrolytes

The rapid development of the Internet of Things (IoT) demands reliable and long-term energy supply to microelectronic devices distributed over the network with high power performance and less maintenance required.[1] Micro-supercapacitors (MSCs) have come to the foreground as miniaturized energy storage devices showing outstanding power density and long cycle life. However, the low cell voltage and low energy density remain major bottleneck that prevents their adoption in real device applications. To this end, several studies have been devoted to the engineering of MSC electrode materials and structural architecting of current collectors within the limited available footprint. This approach could offer opportunities to enhance the electrochemically active surface and mass loading of active materials with fast ion diffusion kinetics, leading to high areal energy density performance.[2] Although several efforts have been put forth in this direction, the complex synthesis route, unfavourable interfacial and mechanical stability of the electrode, electrolyte compatibility issues, etc. remain an arduous challenge.[3] The low cell voltage is another major issue preventing from achieving high energy density values in MSCs as it is directly linked to the electrochemical stability window (ESW) of the electrolytes used.[4] In addition, liquid-state electrolytes currently employed are inappropriate for the microfabrication route as it is prone to evaporation, leakage, and potential safety issues. Hence, a lot of research attention has been given to developing solid-state electrolytes able to afford large operational windows and help promote the application of on-chip MSCs. In this work, we have demonstrated the use of protic ionic liquid (PIL)-based electrolytes able to provide pseudocapacitance in hydrous ruthenium dioxide (RuO2) electrodeposited on interdigitated MSC substrates with extended operational cell voltage. As a pseudocapacitive material, RuO2 exhibits excellent conductivity, high electrochemical reversibility, and cycling stability.[5] On the other hand, as room temperature molten salts, PILs help to overcome evaporation and encapsulation problems associated with the conventional aqueous electrolytes and flammability and safety issues linked to common organic electrolytes.[6-9] We explored pyrrolidinium-based PILs with varying alkyl substitutions, their structure-property, and electrochemical studies for RuO2 MSCs. To further expand the use of these PILs in real devices, 3D MSCs with higher active material mass loading was realised using interdigitated porous Au current collector substrates. The PIL-based porous RuO2 MSCs showed superior charge storage and higher energy density performance as compared to conventional aqueous electrolytes. To envision the practical application of RuO2 MSCs and their subsequent integration with microelectronic devices, ionogel-based solid-state electrolytes were developed. This work opens pathways to develop micro-supercapacitors exhibiting high energy and power density by combining pseudocapacitive metal oxide-based active materials and protic ionic-liquid-based non-aqueous electrolytes and help their integration with on-chip IoT devices.