Recent Submissions

  • Key factors in developing controlled closed ecosystems for lunar missions

    Ortega-Hernandez, José María; Qiu, Dan; Pla-García, Jorge; Yuanxun, Zhang; Martinez-Frias, Jesús; Long, Xiao; Sanchez-Rodriguez, Eva; Hernandez-Narvaez, Juan; Xie, Gengxin; Alberquilla, Fernando; et al. (Elsevier, 2024-05-09)
    The utilization of in-situ resources will be crucial for the survival of astronauts in space. Therefore, plants and crops will be important for humans in space as they serve as food, provide oxygen, and remove carbon dioxide, enhancing habitability. The aim of this research is to explore the growth of crops on celestial bodies prior to human arrival. The paper outlines the creation of a novel capsule by Green Moon Project (GMP) designed to meet essential criteria for monitoring and enhancing crop cultivation on the lunar terrain, tackling key obstacles such as self-propagation, fluctuating light patterns, water provision, and monitoring germination and growth stages. The Center of Space Exploration (hereafter COSE) managed to sprout the first seed on another celestial body during the Chang’e 4 mission on the Moon in January 2019. This achievement means an important step in space agriculture and widens the biological research of crops that will sustain future crewed missions and human bases in space. Space farming requires greater understanding if humans are to survive in space without constant contact from Earth and that is why GMP goals are aligned COSE’s. Therefore, GMP and COSE work in synergy to boost the research of space farming, future crops, habitability, and close controlled environmental systems. Due to the similarities between both projects, both teams decided to join efforts and cooperate in future space missions. To provide scientific support and technical solutions for future long-term crewed exploration missions, it is mandatory to conduct ground verification experiments of controllable extraterrestrial ecosystems.
  • Correction: Resolving nanoscopic structuring and interfacial THz dynamics in setting cements

    Song, Fu V.; Yang, Bin; Di Tommaso, Devis; Donnan, Robert S.; Chass, Gregory A.; Yada, Rickey Y.; Farrar, David H.; Tian, Kun V.; Queen Mary University of London; University of Chester; The University of British Columbia; McMaster University; Sapienza University of Rome (Royal Society of Chemistry, 2022-10-04)
    Correction for ‘Resolving nanoscopic structuring and interfacial THz dynamics in setting cements’ by Fu V. Song et al., Mater. Adv., 2022, 3, 4982–4990, https://doi.org/10.1039/D1MA01002F.
  • Axial algebras of Jordan and Monster type

    McInroy, Justin; Shpectorov, Sergey; University of Chester; University of Birmingham (Cambridge University Press, 2024)
    Axial algebras are a class of non-associative commutative algebras whose properties are defined in terms of a fusion law. When this fusion law is graded, the algebra has a naturally associated group of automorphisms and thus axial algebras are inherently related to group theory. Examples include most Jordan algebras and the Griess algebra for the Monster sporadic simple group. In this survey, we introduce axial algebras, discuss their structural properties and then concentrate on two specific classes: algebras of Jordan and Monster type, which are rich in examples related to simple groups.
  • Existence, uniqueness and regularity for a semilinear stochastic subdiffusion with integrated multiplicative noise

    Li, Ziqiang; Yan, Yubin; Lyuliang University; University of Chester (Springer, 2024-02-21)
    We investigate a semilinear stochastic time-space fractional subdiffusion equation driven by fractionally integrated multiplicative noise. The equation involves the ψ-Caputo derivative of order α∈(0, 1) and the spectral fractional Laplacian of order β∈(12, 1]. The existence and uniqueness of the mild solution are proved in a suitable Banach space by using the Banach contraction mapping principle. The spatial and temporal regularities of the mild solution are established in terms of the smoothing properties of the solution operators.
  • Analysis of the Utilisation of Herbaceous Biomass Streams for Small-Scale Combined Heat and Power Systems: A Comparative Study of UK and Pakistan

    Brammer, John G.; Latif, Mubashra (University of Chester, 2023-05)
    The project scope combined the author`s interest, those of her industrial research partner Biogen Systems Ltd., and those of Professor John Brammer of the University of Chester, UK. The project`s main aim was to find a cost-effective, yet sustainable strategy to upgrade the quality of low-quality herbaceous biomass streams to feed Biogen`s BCHP units without any ash-related problems such as ash melting and clinker formation. This involved pursuing a theoretical understanding and lab-scale combustion experimental investigation of the ash melting characteristics of herbaceous biomass streams of significant interest (grass cuttings, miscanthus, corn cobs, and mango stones) with different additive streams in two country locations which reflect the affiliations of the author, and which are quite different: namely Pakistan and the UK. SEM-EDS and XRD techniques were used with ternary-phase diagrams for determination of elemental and crystalline phase composition analysis of combusted ash residues. Investigations revealed that the formation of low-melting eutectic mixtures of phosphates and silicates is the primary underlying reason for ash melting of non-woody biomass streams which can be avoided by the addition of anti-sintering additives. Furthermore, eggshells can successfully replace Ca-based commercially available additives when in raw and calcined form. Another main objective of this work was to develop an Excel-base financial business model to present a framework that would improve the profitability of Biogen`s BCHP units’ deployment in the UK and Pakistan. For this, different scenarios were studied, and the effects of main logistic variables were quantified on the profitability of Biogen`s units. Results of the financial model indicate that for the UK-based operation of Biogen`s BCHP units, wood chips would serve as the most economically beneficial feedstock, followed by miscanthus pellets, while for Pakistan, waste grass pellets would be the most financially suitable feedstock, followed by corn stover pellets. Food processing companies in Pakistan can generate electricity that is cheaper than the grid-sourced power by Biogen`s E3 BCHP units by utilising on-site available free-cost fruit waste (such as corn cobs and mango waste). Accordingly, the potential contribution of this research is the elimination of the technoeconomic barriers faced by the mass-scale deployment of high-temperature biomass-fuelled thermochemical systems by identification of eggshells as a potential anti-sintering additive and investigation of the effect of cost estimates for different feedstocks for E3 operation. There is a strong need for further research to be done focusing on the gaseous emissions encompassing the utilisation of grass and its mixtures with additives and other biofuels at high-temperature thermochemical systems.
  • Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud Compression

    Adhuran, Jayasingam; Khan, Nabeel; Martini, Maria; Kingston University London; University of Chester (MDPI, 2024-02-21)
    Neuromorphic Vision Sensors (NVSs) are emerging sensors that acquire visual information asynchronously when changes occur in the scene. Their advantages versus synchronous capturing (frame-based video) include a low power consumption, a high dynamic range, an extremely high temporal resolution, and lower data rates. Although the acquisition strategy already results in much lower data rates than conventional video, NVS data can be further compressed. For this purpose, we recently proposed Time Aggregation-based Lossless Video Encoding for Neuromorphic Vision Sensor Data (TALVEN), consisting in the time aggregation of NVS events in the form of pixel-based event histograms, arrangement of the data in a specific format, and lossless compression inspired by video encoding. In this paper, we still leverage time aggregation but, rather than performing encoding inspired by frame-based video coding, we encode an appropriate representation of the time-aggregated data via point-cloud compression (similar to another one of our previous works, where time aggregation was not used). The proposed strategy, Time-Aggregated Lossless Encoding of Events based on Point-Cloud Compression (TALEN-PCC), outperforms the originally proposed TALVEN encoding strategy for the content in the considered dataset. The gain in terms of the compression ratio is the highest for low-event rate and low-complexity scenes, whereas the improvement is minimal for high-complexity and high-event rate scenes. According to experiments on outdoor and indoor spike event data, TALEN-PCC achieves higher compression gains for time aggregation intervals of more than 5 ms. However, the compression gains are lower when compared to state-of-the-art approaches for time aggregation intervals of less than 5 ms.
  • Alzheimer Brain Imaging Dataset Augmentation Using Wasserstein Generative Adversarial Network

    Ilyas, Kulsum; Hussain, B. Zahid; Andleeb, Ifrah; Aslam, Asra; Kanwal, Nadia; Ansari, Mohammad Samar; Aligarh Muslim University; University of Leeds; Keele University; University of Chester (Springer, 2024-02-25)
    Deep learning models have evolved to be very efficient and robust for several computer vision applications. To harness the benefits of state-of-the-art deep networks in the realm of disease detection and prediction, it is imperative that high-quality datasets be made available for the models to train on. This work recognizes the dearth of training data (both in terms of quality and quantity of images) for using such networks for the detection of Alzheimer’s disease. It is proposed to employ a Wasserstein Generative Adversarial Network (WGAN) for generating synthetic images for augmentation of an existing Alzheimer brain image dataset. It is shown that the proposed approach is indeed successful in generating high-quality images for inclusion in the Alzheimer image dataset potentially making the dataset more suited for training high-end models.
  • FireNet-Tiny: Very-Low Parameter Count High Performance Fire Detection Model

    Oyebanji, Olalekan J.; Oliver, Stefy; Ogonna, Chukwuka E.; Aslam, Asra; Ansari, Mohammad Samar; University of Chester; University of Leeds (Springer, 2024-02-25)
    In daily life, fire threats result in significant costs on the ecological, social, and economic levels. It is essential to outfit the assets with fire prevention systems due to the sharp rise in the frequency of fire mishaps. To prevent such mishaps, several studies have been conducted to develop optimal and potent fire detection models. While the earliest methods were thermal/chemical in nature, image processing was later applied for identification of fire. More recent methods have taken advantage of the significant advancements in deep learning models for computer vision. However, in order to maintain a suitable inference time (leading towards real-time detection) and parameter count, the majority of deep learning models have to make trade-offs between their detection speed and detection performance (accuracy/recall/precision). The very lightweight convolution neural network we offer in this paper is specifically designed for the fire detection use case. The proposed model can be embedded in real-time fire monitoring equipment and could also prove potentially useful for future fire monitoring methods such as unmanned aerial vehicles (drones). By further diminishing the trainable parameter count of the model, the fire detection results obtained using the proposed FireNet-Tiny significantly outperform the prior low parameter count models. When tested against FireNet dataset, FireNet-Tiny, which only comprises 261,922 parameters, was shown to have an overall accuracy of 95.75%. In comparison, FireNet-v2 provided 94.95% accuracy with 318,460 parameters.
  • Codes over a ring of order 32 with two Gray maps

    Dougherty, Steven T.; Gildea, Joe; Korban, Adrian; Korban, Adrian; Roberts, Adam (Elsevier, 2024-02-09)
    We describe a ring of order 32 and prove that it is a local Frobenius ring. We study codes over this ring and we give two distinct non-equivalent linear orthogonality-preserving Gray maps to the binary space. Self-dual codes are studied over this ring as well as the binary self-dual codes that are the Gray images of those codes. Specifically, we show that the image of a self-dual code over this ring is a binary self-dual code with an automorphism consisting of 2n transpositions for the first map and n transpositions for the second map. We relate the shadows of binary codes to additive codes over the ring. As Gray images of codes over the ring, binary self-dual [ 70 , 35 , 12 ] codes with 91 distinct weight enumerators are constructed for the first time in the literature.
  • Software Exploitation and Software Protection Measures Enhancing Software Protection via Inter-Process Control Flow Integrity

    Speakman, Lee; Eze, Thaddeus; John, Nigel; Oyinloye, Toyosi A. (University of Chester, 2023-08)
    Computer technologies hinge on the effective functionality of the software component. Unfortunately, software code may have flaws that cause them to be vulnerable and exploitable by attackers. Software exploitation could involve a hijack of the application and deviation of the flow of its execution. Whenever this occurs, the integrity of the software and the underlying system could be compromised. For this reason, there is a need to continually develop resilient software protection tools and techniques. This report details an in-depth study of software exploitation and software protection measures. Efforts in the research were geared towards finding new protection tools for vulnerable software. The main focus of the study is on the problem of Control Flow Hijacks (CFH) against vulnerable software, particularly for software that was built and executed on the RISC-V architecture. Threat models that were addressed are buffer overflow, stack overflow, return-to-libc, and Return Oriented Programming (ROP). Whilst the primary focus for developing the new protection was on RISC-V-based binaries, programs that were built on the more widespread x86 architecture were also explored comparatively in the course of this study. The concept of Control Flow Integrity (CFI) was explored in the study and a proof-of-concept for mitigating ROP attacks that result in Denial of Service is presented. The concept of CFI involves the enforcement of the intended flow of execution of a vulnerable program. The novel protection is based on the CFI concept combined with Inter-process signalling (named Inter-Process Control Flow Integrity (IP-CFI)). This technique is orthogonal to well-practised software maintenance such as patching/updates and is complementary to it providing integrity regardless of exploitation path/vector. In evaluating the tool, it was applied to vulnerable programs and found to promptly identify deviations in vulnerable programs when ROP attacks lead to DoS with an average runtime overhead of 0.95%. The system on which the software is embedded is also protected as a result of the watchdog in the IP-CFI where this kind of attack would have progressed unnoticed. Unlike previous CFI models, IP-CFI extends protection outside the vulnerable program by setting up a mutual collaboration between the protected program and a newly written monitoring program. Products derived in this study are software tools in the form of various Linux scripts that can be used to automate several functionalities, two RISC-V ROP gadget finders (RETGadgets & JALRGadget), and the software protection tool IP-CFI. In this report, software is also referred to as binary, executable, application, program or process.
  • FireNet-Micro: Compact Fire Detection Model with High Recall

    Ansari, Mohammad Samar; University of Chester; University of Leeds
    Fire occurrences and threats in everyday life incur substantial costs on ecological, economic, and even social levels. It is crucial to equip establishments with fire prevention systems due to the notable increase in fire incidents. Numerous studies have been conducted to develop efficient and optimal fire detection models in order to prevent such mishaps. Initially, thermal/chemical methods were used, but later, image processing techniques were also employed to identify fire occurrences. Recent approaches have capitalized on the advancements in deep learning models for computer vision. However, most deep learning models face a trade-off between detection speed and performance (accuracy/recall/precision) to maintain a reasonable inference time (for real-time applications) and parameter count. In this paper, we present a bespoke and highly lightweight convolutional neural network specifically designed for fire detection. This model can be integrated into real-time fire monitoring equipment and potentially applied in future methods suhc as CCTV surveillance cameras, traffic lights, and unmanned aerial vehicles (drones) for fire monitoring in futuristic smart city scenarios. Despite having significantly fewer trainable parameters, our customized model, FireNet-Micro, outperforms existing low-parameter-count models in fire detection. When evaluated on the FireNet dataset, FireNet-Micro, with only 171,234 parameters, achieved an impressive overall accuracy of 96.78%. In comparison, FireNet-v2 attained 94.95% accuracy with 318,460 parameters (which is almost double the parameter count of the proposed FireNet-Micro).
  • Deep Learning Based Lightweight Model for Brain Tumor Classification and Segmentation

    Andleeb, Ifrah; Hussain, B. Zahid; Ansari, Salik; Ansari, Mohammad Samar; Kanwal, Nadia; Aslam, Asra; Aligarh Muslim University; University of Chester; Keele University; University of Leeds (Springer, 2024-02-01)
    This paper presents two lightweight deep learning models for efficient detection and segmentation of brain tumors from MRI scans. A custom-made Convolutional Neural Network (CNN) is designed for identification of four different classes of brain tumors viz. Meningioma, Glioma, Pituitary brain tumor and normal (no tumor). Furthermore, another tailor-made lightweight model is presented for the segmentation of the tumor from the Magnetic Resonance Imaging (MRI) scans. The output of the segmentation model is the ‘mask’ depicting the tumor region. The overall performance in terms of detection accuracy, and segmentation accuracy, for the two models is found to be approximately 95% for both the cases individually. The proposed models are worthy additions to the existing literature on brain tumor classification and segmentation models due to their low-parameter count which make the models amenable for deployment on resource-constrained edge hardware.
  • Coumarin‐Based Light‐Responsive Composite Nanochannel Membranes for Precise Controlled Release of Pesticides

    Gong, Jue‐Ying; Zhou, Xing‐Long; Faraj, Yousef; Zou, Lin‐Bing; Zhou, Chang‐Hai; Xie, Rui; Wang, Wei; Liu, Zhuang; Pan, Da‐Wei; Ju, Xiao‐Jie; et al. (Wiley, 2024-01-28)
    The precise, controllable, and safe application of pesticides can effectively reduce pesticide consumption and minimize chemical pollution at the source. Here, a light‐responsive controlled‐release system with flexible control, precise release, easy recovery, and suitability for future pesticide application in aquatic environments is proposed. The system precisely controls the release of pesticides through a light‐responsive composite nanochannel membrane (CTC@SNM/PET) with reactive coumarin derivatives (CTC) as gating molecules. The prepared nanochannel membrane has an ultrathin thickness of 67.5 nm and well‐ordered vertical nanochannels with a uniform size of 1.9 nm, providing a prerequisite for precise molecular gating and high permeability for mass transport. CTC monomers can realize cycloaddition/cyclocracking and nanochannel closing/opening to control the release of pesticides by controlling 365/254 nm ultraviolet light irradiation. As a proof of concept, the light‐responsive controlled‐release system based on CTC@MSF/PET against Saprolegnia parasitica achieves an inhibition rate of more than 95% and reduces pesticide residues by 56.5% compared to the control group. The proposed membrane system has great application potential to easily enable remote, quantitative, timed, and positioned pesticide application, thereby reducing pesticide residues and providing a prospective approach to reducing environmental and human risks.
  • Numerical Approximation for a Stochastic Fractional Differential Equation Driven by Integrated Multiplicative Noise

    Hoult, James; Yan, Yubin; University of Chester (MDPI, 2024-01-23)
    We consider a numerical approximation for stochastic fractional differential equations driven by integrated multiplicative noise. The fractional derivative is in the Caputo sense with the fractional order α∈(0,1), and the non-linear terms satisfy the global Lipschitz conditions. We first approximate the noise with the piecewise constant function to obtain the regularized stochastic fractional differential equation. By applying Minkowski’s inequality for double integrals, we establish that the error between the exact solution and the solution of the regularized problem has an order of O(Δtα) in the mean square norm, where Δt denotes the step size. To validate our theoretical conclusions, numerical examples are presented, demonstrating the consistency of the numerical results with the established theory.
  • Dissolving microneedle system containing Ag nanoparticle-decorated silk fibroin microspheres and antibiotics for synergistic therapy of bacterial biofilm infection

    Li, Yao; Gong, Jue-Ying; Wang, Po; Fu, Han; Faraj, Yousef; Xie, Rui; Wang, Wei; Liu, Zhuang; Pan, Da-Wei; Ju, Xiao-Jie; et al. (Elsevier, 2024-01-26)
    Most cases of delayed wound healing are associated with bacterial biofilm infections due to high antibiotic resistance. To improve patient compliance and recovery rates, it is critical to develop minimally invasive and efficient methods to eliminate bacterial biofilms as an alternative to clinical debridement techniques. Herein, we develop a dissolving microneedle system containing Ag nanoparticles (AgNPs)-decorated silk fibroin microspheres (SFM-AgNPs) and antibiotics for synergistic treatment of bacterial biofilm infection. Silk fibroin microspheres (SFM) are controllably prepared in an incompatible system formed by a mixture of protein and carbohydrate solutions by using a mild all-aqueous phase method and serve as biological templates for the synthesis of AgNPs. The SFM-AgNPs exert dose- and time-dependent broad-spectrum antibacterial effects by inducing bacterial adhesion. The combination of SFM-AgNPs with antibiotics breaks the limitation of the antibacterial spectrum and achieves better efficacy with reduced antibiotic dosage. Using hyaluronic acid (HA) as the soluble matrix, the microneedle system containing SFM-AgNPs and anti-Gram-positive coccus drug (Mupirocin) inserts into the bacterial biofilms with sufficient strength, thereby effectively delivering the antibacterial agents and realizing good antibiofilm effect on Staphylococcus aureus-infected wounds. This work demonstrates the great potential for the development of novel therapeutic systems for eradicating bacterial biofilm infections.
  • Designing defect enriched Bi2Ti2O7/C3N4 micro-photo-electrolysis reactor for photo-Fenton like catalytic reaction

    Yan, Yuan; Hu, Wenyuan; Xie, Xinyu; Faraj, Yousef; Yang, Wulin; Xie, Ruzhen; Sichuan University; Southwest University of Science and Technology; Russian Federation Kaluga No. 13 School; University of Chester; Peking University; Tianfu Yongxing Laboratory, Chengdu (Elsevier, 2023-10-28)
    Among various advanced oxidation processes, photo-Fenton like catalysis, which couples solar energy with Fenton-like catalysis to generate highly reactive species for wastewater decontamination, has attracted broad interests. However, photo-Fenton catalysts usually suffer from poor pH adaptability, metal leaching and photogenerated charge recombination. Herein, a novel defect-enriched Bi2Ti2O7/C3N4 (BTO/CN) heterojunction is prepared via ball milling-thermal treatment method and used as a durable photo-Fenton like catalyst to degrade phenol in water. The BTO/CN heterojunction shows an excellent optical absorption capacity, and a superior e--h+ separation efficiency. With the addition of PMS, a micro-photo-electrolysis reactor can be formed in the BTO/CN, rendering it high photocatalytic activity, excellent tolerance to environmental condition and exceptional stability. The BTO/CN micro-photo-electrolysis reactor exhibits superior performance in phenol removal and excellent tolerance towards salt ions. Non-radical pathway and radical dotOH oxidation are demonstrated to contribute to phenol degradation in the BTO/CN heterojunction photo-Fenton-like system. The PMS can simultaneously boost the interfacial charge transmission from BTO to CN forming internal BTO photoanode and CN photocathode, leading to sustainable photocatalytic performance without secondary pollution. This work successfully demonstrates a feasible strategy to develop solar energy assisted Fenton-like catalyst for efficient water decontamination, which holds a great promise towards practical photo-Fenton water decontamination.
  • Dual-WGAN Ensemble Model for Alzheimer’s Dataset Augmentation with Minority Class Boosting

    Ansari, Mohammad Samar; Ilyas, Kulsum; Aslam, Asra; University of Chester; Aligarh Muslim University; University of Leeds (IEEE, 2023-11-20)
    Deep learning models have become very efficient and robust for several computer vision applications. However, to harness the benefits of state-of-art deep networks in the realm of disease detection and prediction, it is crucial that high-quality datasets be made available for the models to train on. This work recognizes the lack of training data (both in terms of quality and quantity of images) for using such networks for the detection of Alzheimer’s Disease. To address this issue, a Wasserstein Generative Adversarial Network (WGAN) is proposed to generate synthetic images for augmentation of an existing Alzheimer brain image dataset. The proposed approach is successful in generating high-quality images for inclusion in the Alzheimer image dataset, potentially making the dataset more suitable for training high-end models. This paper presents a two-fold contribution: (i) a WGAN is first developed for augmenting the non-dominant class (i.e. Moderate Demented) of the Alzheimer image dataset to bring the sample count (for that class) at par with the other classes, and (ii) another lightweight WGAN is used to augment the entire dataset for increasing the sample counts for all classes.
  • Ferroelectric anomaly of perovskite layer structured Pb2+-doped Sr2Nb2O7 ceramics

    Liu, Lintao; Chen, Tao; Ouyang, Delai; Yue, Yajun; Yang, Bin; Yan, Haixue; Abrahams, Isaac; Fu, Zhengqian; Liang, Ruihong; Zhou, Zhiyong; et al. (Wiley, 2024-01-09)
    The spontaneous polarization of perovskite layer structured Sr2Nb2O7 ferroelectrics (FEs) is originated from mainly the oxygen octahedral rotations and partially the displacement of Sr2+ ions. However, there is FE anomaly of showing the typical characteristics of antiferroelectric (AFE)‐like behavior with double polarization–electric field hysteresis loops of Sr2Nb2O7 ceramics by Pb doping. Here, combinations of low frequency and sub‐terahertz band dielectric measurements under applied DC field reveal field‐induced transition from AFE to FE structure. Temperature dependence of dielectric constant suggested a second‐order phase transition near 215°C, which further supported by the lattice parameters and thermal expansion coefficient extracted from the variable temperature X‐ray diffraction in the heating and cooling processes. The selected area electron diffraction results show no new superlattice spots are observed along [1 0 0] zone axis related to c direction. We proposed a model based on octahedral tilting/rotation that accounted for the incommensurate lattice modulation in the c direction for Sr2Nb2O7 system by Pb doping. All the results show AFE‐like behavior be likely to origin from the electron structures of Pb2+ with a 6s2 lone pair. Our results gave us a new concept provide possibilities for the design of AFE‐like materials in layer structured compounds with super high FE Curie point.
  • An Ultra-Energy-Efficient Reversible Quantum-Dot Cellular Automata 8:1 Multiplexer Circuit

    Alharbi, Mohammed; Edwards, Gerard; Stocker, Richard; Liverpool John Moores University; University of Chester (MDPI, 2024-01-16)
    Energy efficiency considerations in terms of reduced power dissipation are a significant issue in the design of digital circuits for very large-scale integration (VLSI) systems. Quantum-dot cellular automata (QCA) is an emerging ultralow power dissipation approach, distinct from traditional, complementary metal-oxide semiconductor (CMOS) technology, for building digital computing circuits. Developing fully reversible QCA circuits has the potential to significantly reduce energy dissipation. Multiplexers are fundamental elements in the construction of useful digital circuits. In this paper, a novel, multilayer, fully reversible QCA 8:1 multiplexer circuit with ultralow energy dissipation is introduced. The power dissipation of the proposed multiplexer is simulated using the QCADesigner-E version 2.2 tool, describing the microscopic physical mechanisms underlying the QCA operation. The results show that the proposed reversible QCA 8:1 multiplexer consumes 89% less energy than the most energy-efficient 8:1 multiplexer circuit previously presented in the literature.
  • Mechanism of anodic activation of chloride to generate singlet oxygen for fast organic removal using an innovative anode

    Zhang, Weijuan; Lin, Hui; Faraj, Yousef; Xie, Ruzhen; Sichuan University; Dongguan University of Technology; University of Chester (Elsevier, 2024-01-19)
    Electrochemical persulfate activation (E-PS) has recently emerged as a highly effective advanced oxidation process in water decontamination. However, the presence of chloride ions (Cl−) in waters can accelerate anodic corrosion as well as lead to the formation of toxic chlorinated byproducts (i.e., ClO4 −), limiting its practical application. In this study, we introduce a novel Nd/Bi@SnO2 anode to construct E-PS, which exhibits high stability in chloride-containing water with a long-expected service lifetime of 13.7 years. The Nd/Bi@SnO2 electrode can effectively convert Cl− to reactive chlorine with the assistance of PMS, triggering singlet oxygen (1O2) generation for superior organic removal while avoiding toxic chlorinated byproducts (i.e., ClO4 −) generation as well as greatly reducing the energy consumption. Comprehensive structural and electrochemical characterization results demonstrate Nd/Bi co-doping introduces oxygen vacancy on Nd/Bi@SnO2, enabling the anode with high oxygen evolution potential, excellent conductivity and superior stability. Scavenging experiments and electron paramagnetic resonance illustrate the generation of various reactive species in the system, among which 1O2 predominantly contributes to organic removal and results in harmless intermediates. This innovative approach transforms Cl− into ROSs for eco-friendly, energy-efficient water decontamination.

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