Low Energy Consumption Research Articles

Efficient Separation of Oil/Water by a Biodegradable and Superhydrophobic Composite Based on Loofah and Rice Straw

Membrane filtration is one of the preferred choices for petroleum wastewater disposal due to its simplicity and low energy consumption. In this paper, a biodegradable superhydrophobic membrane based on loofah and rice straw (LF-RS) was prepared and modified with dodecyltriethoxysilane to improve its stability, morphology, and performance. The membrane showed an efficiency of 99.06% for oil/water separation with an average water flux of 2057.37 Lm−2h−1 and a tensile strength of 11.19 MPa. The tensile strength of the LF-RS membrane was 322.47% higher than that of the PVDF membrane and 126.58% higher than that of the commercially available nitrocellulose membrane. Through molecular simulations, we showed a 96.3% reduction in interaction energy between water and membrane post-modification, which is beneficial for increasing the contact angle and separation performance. This study provides an option for the large-scale, cost-effective fabrication of eco-friendly membranes for pollutant removal.

On-chip deep subwavelength optomagnetic bistable memory based on inverse Faraday effect related nonlinearity in graphene waveguide ring resonators

Integrated optical devices that possess bistable characteristics are key elements for optical digital signal processing and all-optical computing. In this paper we report on an optomagnetic bistable memory based on the inverse Faraday effect related nonlinearity in graphene waveguide ring resonators. The effective magnetic field via the plasmon-induced inverse Faraday effect in the graphene resonator has two discrete stable states that represent logic one and logic zero and magnetic switching for memory operation can be achieved by injecting an optical pulse and momentarily blocking the bias input light. Our proposed optomagnetic bistable device based on a graphene magnetoplasmonic configuration has great potential for the development of next generation magnetic memory. It has benefits of ultrafast magnetization switching, simplicity of the considered structure, on-chip compatibility, and a deep subwavelength platform. It also enhances the nonlinear interaction due to strong confinement of the graphene plasmon modes and high-quality factor of traveling modes in the graphene ring resonator, and tunability and robustness by adjusting the gate voltages of the graphene sheets. These advantages will satisfy the demand for fast magnetization switching, low-energy consumption, and high-density recording required in future magnetic memories. Published by the American Physical Society 2024

A Low-Cost Modulated Laser-Based Imaging System Using Square Ring Laser Illumination for Depressing Underwater Backscatter

Underwater vision data facilitate a variety of underwater operations, including underwater ecosystem monitoring, topographical mapping, mariculture, and marine resource exploration. Conventional laser-based underwater imaging systems with complex system architecture rely on high-cost laser systems with high power, and software-based methods can not enrich the physical information captured by cameras. In this manuscript, a low-cost modulated laser-based imaging system is proposed with a spot in the shape of a square ring to eliminate the overlap between the illumination light path and the imaging path, which could reduce the negative effect of backscatter on the imaging process and enhance imaging quality. The imaging system is able to achieve underwater imaging at long distance (e.g., 10 m) with turbidity in the range of 2.49 to 7.82 NTUs, and the adjustable divergence angle of the laser tubes enables the flexibility of the proposed system to image on the basis of application requirements, such as the overall view or partial detail information of targets. Compared with a conventional underwater imaging camera (DS-2XC6244F, Hikvision, Hangzhou, China), the developed system could provide better imaging performance regarding visual effects and quantitative evaluation (e.g., UCIQUE and IE). Through integration with the CycleGAN-based method, the imaging results can be further improved, with the UCIQUE increased by 0.4. The proposed low-cost imaging system with a compact system structure and low consumption of energy could be equipped with platforms, such as underwater robots and AUVs, to facilitate real-world underwater applications.

Stable Millivolt Range Resistive Switching in Percolating Molybdenum Nanoparticle Networks.

To overcome the limitations of the conventional Von Neumann architecture, inspiration from the mammalian brain has led to the development of nanoscale neuromorphic networks. In the present research, molybdenum nanoparticles (NPs), which were produced by means of gas phase condensation based on magnetron sputtering, are shown to be the constituents of electrically percolating networks that exhibit stable, complex, neuron-like spiking behavior at low potentials in the millivolt range, satisfying well the requirement of low energy consumption. Characterization of the NPs using both scanning electron microscopy and scanning transmission electron microscopy revealed not only pristine shape, size, and density control of Mo NPs but also a preliminary proof of the working mechanism behind the spiking behavior due to filament formations. Furthermore, electrostatics COMSOL Multiphysics simulations of the morphology of the NPs provided evidence that the stable switching is due to the as-deposited stellate Mo NPs creating high electric field strengths, while keeping them separated, not seen before in other percolating networks based on spherical NPs. Hence, our results show the working mechanism behind switching in percolating Mo NP networks and show that they are very promising for realistic neuromorphic systems.

A mapping-free natural language processing-based technique for sequence search in nanopore long-reads.

In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough dose estimates in thousands of donors. The new generation in-situ mapper, fast and of low energy consumption, working at the level of single nanopore output, is in demand. We aim to create a sequence identification tool that utilizes natural language processing techniques and ensures a high level of negative predictive value (NPV) compared to the classical approach. The training dataset consisted of RNA sequencing data from 6 samples. Multiple natural language processing models were examined, differing in the type of dictionary components (word length, step, context) as well as the encoding length and number of sequences required for algorithm training. The best configuration analyses the entire sequence and uses a word length of 3 base pairs with one-word neighbor on each side. For the considered FDXR gene, the achieved mean balanced accuracy (BACC) was 98.29% and NPV was 99.25%, compared to minimap2's performance in a cross-validation scenario. The next stage focused on exploring the dictionary components and attempting to optimize it, employing statistical techniques as well as those relying on the explainability of the decisions made. Reducing the dictionary from 1024 to 145 changed BACC to 96.49% and the NPV to 98.15%. Obtained model, validated on an external independent genome sequencing dataset, gave NPV of 99.64% for complete and 95.87% for reduced dictionary. The salmon-estimated read counts differed from the classical approach on average by 3.48% for the complete dictionary and by 5.82% for the reduced one. We conclude that for long Oxford nanopore reads, a natural language processing-based approach can reliably replace classical mapping when there is a need for fast, reliable and energy and computationally efficient targeted mapping of a pre-defined subset of transcripts. The developed model can be easily retrained to identify selected transcripts and/or work with various long-read sequencing techniques. Our results of the study clearly demonstrate the potential of applying techniques known from classical text processing to nucleotide sequences.

Strategically Designed Uniform MOF-Derived Nanoporous Carbon Aerogel for Efficient Solar-Driven Desalination by Control of Hydrophilicity and Thermal Conductivity.

Desalination techniques using the photothermal effect hold significant potential for producing fresh water from saline or polluted sources due to their low energy consumption. In the case of commercialized carbon materials are related to heat loss resulting from high thermal conductivity, and metal particles still have trouble in commercialization or cost-effectiveness. This is because a photothermal desalination evaporator must simultaneously exhibit high water evaporation performance, excellent energy conversion efficiency, sufficient hydrophilicity, and low heat loss. In this work, developing an efficient in situ energy utilization technology that instant light to heat energy conversion system based on ZIF-8/agarose-derived carbon aerogels, achieved by controlling hydrophilicity, thermal conductivity, and light absorption properties is reported. The carbon aerogel demonstrates excellent performances of improved capillary force, structural stability, and cost-effectiveness. The designed carbon aerogel, with a high surface area (524 m2 g-1), adequate hydrophilicity, and low density (0.07g cm-3), is buoyant enough to float on the water. A water evaporation efficiency of 1.53kg m-2 h-1 under 1 sun and a light-to-heat conversion of 85% are achieved, along with effective salt blocking through the size-controlled uniform ZIF-8 nanoparticles and optimized composition with agarose.

Development and Assessment of a Batch Type Ohmic Heating System for Milk Processing

This study aims to develop and assess the performance of a batch-type ohmic heating system for the thermal processing of cow and buffalo milk, particularly investigating whether the ohmic system affects the nutritional and microbial quality of the milk. An experimental setup was fabricated with a 7-liter capacity, utilizing an agitator as one electrode and the vessel surface as the other. The system was tested for heating milk from 32°C to target temperatures of 75°C and 92°C. Key processing parameters, including agitator speed and applied voltage, were optimized for both milk types. The physicochemical and microbial properties of the ohmically heated milk were evaluated, along with performance metrics such as heating rates, power consumption, and system efficiency. The developed ohmic heating system achieved heating rates of 4.29°C/min for cow milk and 2.85°C/min for buffalo milk. Cow milk demonstrated lower energy consumption, requiring less time and voltage compared to buffalo milk, with system performance coefficients of 58% for cow milk and 50% for buffalo milk. However, fouling on the agitator surface was identified as a challenge for industrial applications. Importantly, the ohmic heating process preserved the nutritional content while effectively reducing microbial load, confirming its potential for dairy processing. In conclusion, the batch-type ohmic heating system exhibited high efficiency and rapid heating capabilities for both cow and buffalo milk, indicating its viability for commercial use. Future research should focus on addressing fouling issues and further optimizing the system, enhancing the overall potential of ohmic heating technology in the dairy industry.

Multimodal 2D Ferroelectric Transistor with Integrated Perception-and-Computing-in-Memory Functions for Reservoir Computing.

Emerging neuromorphic hardware promises energy-efficient computing by colocating multiple essential functions at the individual component level. The implementation is challenging due to mismatch between the characteristics of multifunctional devices and neural networks. Here, we demonstrate an artificial synapse based on a 2D α-phase indium selenide that exhibits integrated perception-and-computing-in-memory functions in a single-transistor setup, serving as a basic building block for reservoir computing. Extending to the array architecture enables concurrent image-sensing and memory. Further, we implement multimode deep-reservoir computing with adjustable nonlinear transformation and multisensory fusion using this core device. In the lane-keeping-assistance task for an unmanned vehicle, the system demonstrates ∼104 times lower energy consumption and significantly boosted data throughput compared to the state-of-the-art graphics processors. The demonstrated perception-and-computing-in-memory (PCIM) functions at a single-transistor level shows the feasibility of implementing ultrascalable, resource-efficient hardware for brain-inspired computing.

Proton Pool for the Mitigation of Salt Precipitate Enhancing CO2 Electroreduction in a Flow Cell

Flow cells featuring a gas diffusion electrode (GDE) have emerged as an attractive platform for electrochemical CO2 reduction, offering high current densities (~300 mA·cm−2) and low energy consumption. However, the formation of salt precipitates, particularly carbonate and bicarbonate, poses a significant deficiency by reducing the cell’s operational longevity. In this study, we present a novel approach to mitigate salt precipitates in real-time through acid–base interaction. Recovery efficiency and partial current density of the cell were used to evaluate the capability of removing salt precipitates and the maintenance of CO2 reduction reactions (CO2RRs). It was suggested that the direct treatment of intermittent acid rinse recovers the performance of CO2RRs to a large extent (>97%), and the modification of the proton exchange resin reduces the reduction rate of partial current densities to 1/15 than that of the unmodified. This improvement enhances the cell’s catalytic performance, enabling the stability test for catalysts within the GDE-based flow cell.

Enhanced Adsorption Kinetics and Capacity of a Stable CeF3@Ni3N Heterostructure for Methanol Electro-Reforming Coupled with Hydrogen Production.

Alkaline methanol-water electrolysis system is regarded as an appealing strategy for electro-reforming methanol into formate and producing hydrogen with low energy-consumption compared with alkaline water electrolysis. However, stability and selectivity under high current densities for practical application remain challenging. Herein, a CeF3@Ni3N nanosheets array anchored on carbon cloth (CeF3@Ni3N/CC) was fabricated. The gradual extrusion of F species from Ni(OH)2 lattices can stabilize hierarchical structure and construct abundant heterostructure interfaces. Moreover, CeF3 can modulate electron distribution of Ni3N, thus simultaneously enhancing the surface adsorption kinetics and capability of methanol and OH-, which is conducive to enhanced methanol oxidation reaction (MOR) activity and selectivity. Therefore, bifunctional CeF3@Ni3N/CC exhibits low potential of 1.43 V at 500 mA cm-2, along with high stability over 72 h and high faradaic efficiency (FEs) in MOR, as well as an overpotential of 76 mV to achieve 50 mA cm-2 for hydrogen evolution reaction (HER). Furthermore, membrane-free CeF3@Ni3N/CC||CeF3@Ni3N/CC cell for MOR||HER delivers high electrocatalytic activity, long-term stability and FEs at high current density of 300 mA cm-2. This study highlights the importance of optimizing surface adsorption behavior of active species, as well as rational design of highly efficient heterostructure electrocatalysts for methanol upgrading coupled with hydrogen production.

Ultra‐Low Power Photosynaptic Devices Based on Persistent Photoconductivity‐Modulated SnO2

AbstractRealizing visual perception performance using robust metal oxide devices is highly desirable for brain‐inspired neuromorphic computing, visual prosthetics, and artificial intelligence. SnO2, a representative transparent semiconductor, has attracted considerable attention for its potential in ultraviolet photodetectors and photosynaptic devices operating in solar‐blind spectral ranges. This problem has been circumvented by lowering the electron concentration and reducing the persistent photoconductivity (PPC), one of the essential properties of photosynaptic devices. In this case, the electron concentration and PPC properties in SnO2 must be controlled independently. This paper reports the successful control of PPC in SnO2 by controllable addition of Zn and Sr ions, which act as acceptors to annihilate the charge carriers and as a reductant to form oxygen vacancies, respectively. Zn and Sr‐added SnO2 exhibits superior synaptic performance with low energy consumption, successfully imitating the biological synapses without a gate electrode. As a result, the PPC remains at about 40% even after 20 seconds of turning off the ultraviolet light, and energy consumption is reduced to 1–10 fJ, similar to energy consumption with biological synapses. A novel optical sensor that can receive analog signals is demonstrated using a photosynaptic device with Zn and Sr‐added SnO2 thin films.

Historical Insights into CO2 Emission Dynamics in Urban Daily Mobility: A Case Study of Lyon’s Agglomeration

CO2 emissions from urban daily mobility play a major role in both environmental degradation, rising economic costs, and sustainability. Reducing these emissions not only advances environmental sustainability but also fosters economic development by enhancing public health, lowering energy consumption, and alleviating the financial strain caused by climate change. Understanding the dynamics of CO2 emissions from urban daily mobility provides valuable historical insights into environmental impacts and economic costs tied to urban development. This study takes a historical perspective, examining changes in CO2 emissions associated with daily mobility in the Lyon agglomeration across two decades, drawing on data from the 1995 and 2006 household travel surveys. Our findings reveal that individual factors such as gender, age, employment status, and income significantly influence CO2 emissions, with males and full-time workers exhibiting higher emissions. Furthermore, household characteristics, including size and vehicle ownership, are critical in shaping emission levels, while urban form variables such as population density and mixed land use demonstrate a negative correlation with emissions, highlighting the importance of urban planning in mitigating CO2 output. The analysis emphasizes that greater accessibility and proximity to essential services are vital in reducing individual emissions. Based on these insights, we discuss the implications for policy design, suggesting targeted strategies to enhance urban mobility, improve public transport accessibility, and promote sustainable urban development. Finally, we outline directions for future research to further explore the intricate relationship between urban characteristics and emissions, ultimately aiming to contribute to the development of effective climate policies.

Organic Solvent Nanofiltration Membrane with In Situ Constructed Covalent Organic Frameworks as Separation Layer

Organic solvent nanofiltration (OSN) technology is advantageous for separating mixtures of organic solutions owing to its low energy consumption and environmental friendliness. Covalent organic frameworks (COFs) are good candidates for enhancing the efficiency of solvent transport and ensuring precise molecular sieving of OSN membranes. In this study, p-phenylenediamine (Pa) and 1,3,5-trimethoxybenzene (Tp) are used to construct, in situ, a TpPa COF skin layer via interfacial polymerization (IP) on a polyimide substrate surface. After subsequent crosslinking and activation steps, a kind of TpPa/polyimide (PI) OSN membrane is obtained. Under optimized fabrications, this OSN membrane exhibits an ethanol permeance of 58.0 LMH/MPa, a fast green FCF (FGF) rejection of 96.2%, as well as a pure n-hexane permeance of 102.0 LMH/MPa. Furthermore, the TpPa/PI OSN membrane exhibits good solvent resistance, which makes it suitable for the separation, purification, and concentration of organic solvents.

Lower Energy Consumption in Multi-CPU Cell-Free Massive MIMO Systems

Under the ideal assumption of deploying only one central processing unit (CPU) in the entire system, cell-free (CF) systems can achieve significant macro-diversity gain, thereby providing uniformly reliable service to each user equipment (UE). However, due to limitations in system scalability and the feasibility of strict phase synchronization, CF systems require a multi-CPU setup and perform coherent transmission at a smaller scale. Moreover, conventional CF systems typically operate in time-division duplex (TDD) mode and utilize statistical channel state information (CSI) for downlink (DL) decoding, but the channel hardening effect is not significant. These factors reduce downlink spectral efficiency (SE) and increase DL transmission time, leading to higher energy consumption in CF systems. To address these issues, we introduce downlink channel estimation (DLCE) in multi-CPU CF systems and derive the approximate achievable DL SE. To reduce DL pilot overhead, we propose an uplink–pilot-reuse-constrained DL pilot allocation principle. Based on this principle, we develop a farthest distance pilot allocation (FDPA) algorithm to mitigate pilot contamination. In addition, leveraging the characteristics of the heuristic distributed power allocation algorithm, we propose two access point (AP) clustering algorithms: one based on CSI (BCSI) and the other based on coherent group size (BCGS). Simulation results indicate that the introduction of DLCE significantly improves DL SE in multi-CPU CF massive MIMO systems, while the proposed FDPA algorithm further enhances DL SE. The BCSI and BCGS algorithms also effectively improve DL SE and help reduce energy consumption. By combining DLCE, the FDPA algorithm, and the proposed AP clustering algorithms, the energy consumption of multi-CPU CF systems can be significantly reduced.

Unlocking renewable fuel: Charcoal briquettes production from agro-industrial waste with cassava industrial binders

Increasing global fuel demand has created challenges in sourcing raw materials, making agro-waste biomass utilization for energy production a critical alternative. Therefore, the aim of this study was to investigate the production of charcoal briquettes using different base materials such as coconut shells (TC1), Leucaena leucocephala wood (TC2), and assorted wood charcoal residues (TC3), along with binders derived from cassava industry by-products—namely, tapioca starch (TB1), cassava peel (TB2), and cassava tubers (TB3) at binder content (BC) ratios of 5, 10, 15, and 20 by volume. Initially, assessing the physical properties, including proximate and ultimate analyses, was crucial for predicting effective charcoal production. The TC2 briquettes, using TB2 and TB3 binders at a BC20 concentration, were found to be optimal, displaying a high production capacity and low specific energy consumption. Using cassava peel and tubers as binders not only efficiently uses agricultural by-products, thereby reducing costs and avoiding competition with food resources (TC1) but also results in high-quality briquettes. In scenarios of raw material shortages, TC3 and TC2 can effectively replace TC1. Mechanically, TC3 and TC2 briquettes exhibited superior characteristics. Although BC20 enhances the mechanical properties and facilitates handling, it may compromise heat release. Thermally, a higher BC decreases the charcoal proportion, leading to increased ignition time, reduced calorific value, and heating effectiveness. In conclusion, the successful production of charcoal briquettes from agro-industrial waste using cassava industrial binders presents a viable pathway towards more sustainable energy practices, improves waste management, and adds value to agricultural waste.

Influence of Finishing Process Parameters of HDF Boards on Selected Properties of Coatings in Modern UV Lines and Their Relation to Energy Consumption

This study analyzes the influence of energy generated by emitters on the adhesive properties of varnish coatings in multilayer UV systems. The experimental material, in the form of a cell board finished with UV varnish products, was prepared on a prototype line under the conditions of Borne Furniture in Gorzów Wielkopolski. The roughness and wettability were measured using a OneAttension tensiometer integrated with a topographic module, taking into account the Wenzel coefficient. The adhesion of the examined systems was verified using the PositiTest AT-A automatic pull-off device. Energy consumption by the prototype production line was compared to the standard line, utilizing mercury emitters and mercury emitters with added gallium. Energy consumption was calculated for selected variants. The influence of the Wenzel coefficient on the wettability angle was observed. Significant differences between contact angles (CA and CAc) were noted for coatings formed with sealers (stages I and II). The largest discrepancies, reaching up to 30 degrees, were recorded at the lowest UVA and UVV doses of 26 mJ/cm2. In adhesion tests, values below 1 MPa were obtained. Insufficient energy doses in the curing process of UV systems led to delamination between the coatings. Five variants were selected where delamination within the substrate predominated (˃90% A) and were characterized by the lowest energy consumption in the processes. Topographic images helped identify the presence of various surface microstructures at different stages of the production cycle. The greatest energy savings, up to 50%, were achieved in stages III and IV of the technological process.

Selective Oxidation of Polyesters via PdCu-TiO2 Photocatalysts in Flow.

Catalytic upcycling of plastic wastes offers a sustainable circular economy. Selective conversion of the most widely used polyester, polyethylene terephthalate (PET), under ambient conditions is practically attractive because of low energy consumption and carbon footprint. Here, we report selective, aerobic conversion of PET in a flow reactor using TiO2 photocatalyst modified with atomic Pd and metallic PdCu (Pd1Cu0.4-TiO2) under ambient conditions. We demonstrate that atomically synergistic Pd1Cu0.4-TiO2 exhibits a formate evolution of 4707 μmol g-1 h-1 with a selectivity of 92.3% together with trace COx released. Importantly, we show that this corresponds to 10-103 times greater activity than reported photocatalytic systems. We confirm that synergy between atomic Pd and metallic PdCu boosts directional charge transfer and oxygen-induced C-C cleavage and inhibits product decomposition. We conclude that photocatalytic waste plastic-to-chemical conversion is sustainable via targeted engineering of atomically synergistic catalysts and reaction systems.

Fuzzy Allocation Optimization Algorithm for High-Density Storage Locations with Low Energy Consumptions

The global demand for stored and processed data has surged due to the development of IoTs and similar computational structures, which has led to further energy consumption by concentrated data storage facilities and thus the demands of global energy and environmental needs. The current paper introduces Fuzzy Allocation Optimization Algorithm to mitigate energy consumption in high storage density settings. It uses the principles of Fuzzy logic to determine the best way to assign the tasks in relation to storage density necessity, urgency and energy consumption. Thus, the proposed approach incorporates fuzzy inference systems with multi-objective optimization methods where location of storage is dynamically assessed and assigned according to energy efficiency parameters. The findings of the simulation and case study prove that the algorithm is successful in saving energy while at the same time lowering storage I/O response time, which provides a viable solution to energy issues in evolving data centres. This work satisfies the lack of energy efficient algorithms in high density storage areas and responds to the recent calls for green technology and smart utilization of resources in the energy field. The findings are used in the promotion of significant IT infrastructures towards developing the next generation of energy efficient data centers with respect to Future Internet and evolving energy web environments.

Nitrogen-doped carbon quantum dot-decorated In2O3 synaptic transistors for neuromorphic computing

Nitrogen-doped carbon quantum dots (N-CQDs) are promising materials for electronic devices due to their variable bandgap and structural stability. Here, we integrate N-CQDs into In2O3 synaptic transistors with electrolyte gating, resulting in a hybrid structure. The surface functional groups and defects of N-CQDs empower the charge trapping mechanism, permitting controlled conduction and charge regulation, which are crucial for emulating linear and symmetric artificial synaptic devices. Devices incorporating N-CQDs demonstrate enhanced stability and memory characteristics, low energy consumption, consistent retention, and a significant hysteresis window across multiple voltage cycles. Finally, the study emulates biological synapses and cognitive functions, achieving an energy consumption of 10 fJ per synaptic event and a pattern recognition accuracy of 91.2% on the MNIST dataset in hardware neural networks. This work demonstrates the potential of well-manipulating charge trapping in N-CQDs to develop high-performance, nonvolatile synaptic devices.

Low-Temperature Rapid Drying of Viscous Sludge Via Non-Phase Change Based on Particle High-Speed Self-Rotation and Medium Dispersion in Cyclone

Drying operations are of grave importance to realize the reduction and utilization of sewage sludge resources, but the conventional thermal evaporation drying (TED) technology faces challenges due to the need for a large amount of thermal energy to conquer the phase-change latent heat of moisture. Herein, we report a non-phase change technology based on particle high-speed self-rotation in a cyclone for fast, low-temperature drying of viscous sludge with high moisture contents. Dispersed phase medium (DPM) is introduced into the cyclone self-rotation drying (CSRD) reactor to enhance the dispersion of the viscous sludge. The effects of carrier gas temperature, feeding rate, size, and proportion of DPM particles in the drying process are systematically examined. Under optimal operating conditions, the weighted content of moisture in the viscous sludge could be reduced from 80% to 15.01% in less than 5 s, achieving a high drying efficiency of 95.79%. Theoretical calculations also reveal that 89.26% of the moisture is removed through a phase change pathway, contributing to a 522-fold increase in the drying rate of CSRD compared to TED technology. This investigation presents a sustainable effective approach for high moisture viscous sludge treatment with low energy consumption and carbon emissions.