Energy Efficiency Of Process Research Articles

Seawater alkalization via an energy-efficient electrochemical process for CO2 capture

Electrochemical pH-swing strategies offer a promising avenue for cost-effective and energy-efficient carbon dioxide (CO2) capture, surpassing the traditional thermally activated processes and humidity-sensitive techniques. The concept of elevating seawater's alkalinity for scalable CO2 capture without introducing additional chemical as reactant is particularly intriguing due to its minimal environmental impact. However, current commercial plants like chlor-alkali process or water electrolysis demand high thermodynamic voltages of 2.2 V and 1.23 V, respectively, for the production of sodium hydroxide (NaOH) from seawater. These high voltages are attributed to the asymmetric electrochemical reactions, where two completely different reactions take place at the anode and cathode. Here, we developed a symmetric electrochemical system for seawater alkalization based on a highly reversible and identical reaction taking place at the anode and cathode. We utilize hydrogen evolution reaction at the cathode, where the generated hydrogen is looped to the anode for hydrogen oxidation reaction. Theoretical calculations indicate an impressively low energy requirement ranging from 0.07 to 0.53 kWh/kg NaOH for established pH differences of 1.7 to 13.4. Experimentally, we achieved the alkalization with an energy consumption of 0.63 kWh/kg NaOH, which is only 38% of the theoretical energy requirements of the chlor-alkali process (1.64 kWh/kg NaOH). Further tests demonstrated the system's potential of enduring high current densities (~20 mA/cm2) and operating stability over an extended period (>110 h), showing its potential for future applications. Notably, the CO2 adsorption tests performed with alkalized seawater exhibited remarkably improved CO2 capture dictated by the production of hydroxide compared to the pristine seawater.

Ultrasensitive dim-light neuromorphic vision sensing via momentum-conserved reconfigurable van der Waals heterostructure

Reconfigurable phototransistors featuring bipolar photoresponses are favorable for manipulating high-performance neuromorphic vision sensory. Here, we present a momentum-conserved reconfigurable phototransistor based on the van der Waals heterojunction between methylammonium lead iodide perovskite and two-dimensional Bi2O2Se semiconductor, which exhibits a synergistic interplay of interband hot-carrier transitions and reconfigurable heterointerface band alignments, eventually achieving the ultrahigh bipolar optoelectronic performances with the photoresponsivity of 6×107 AW−1, accompanied by the specific detectivity of 5.2×1011 Jones, and the dynamic range of 110 dB. Moreover, A 3×3 heterotransistor array is fabricated to perform in-sensor analog multiply-accumulate operations even under the challenging dim illumination of 0.1 μWcm−2 that comparable to natural moonlight. The reconfigurable heterotransistor array can be further adopted to enhance the traffic-light detection under dim-light conditions. Our advancement in momentum-conserved reconfigurable heterotransistor signifies a leap forward in real-time, energy-efficient, and low-light image processing for neuromorphic vision sensors.

Influence of green solvents on the recovery of cathode active materials from electrode scraps: A comparative study

Direct recycling of cathode materials from spent Li-ion batteries (LIBs) or electrode scraps necessitates the efficient recovery of active materials from the aluminum foil. The variability in electrode types and recovery processes across previous studies complicates the comparative assessment of the recovery performance of green solvents. In this study, we evaluated the performance of three green solvents—triethyl phosphate (TEP), dihydrolevoglucosenone (Cyrene), and propylene carbonate (PC)—in recovering valuable active materials from industrial-grade cathode scraps. Using ultrasonication, we developed a standardized, energy-efficient recovery process that eliminated the need for conventional stirring and achieved complete cathode delamination from the aluminum foil. Furthermore, we successfully recovered the used green solvents after the process, ensuring their reuse and supporting a circular economy. The recovered materials retained their original morphology, chemical composition, and crystalline structure; however, the presence of surface impurities varied significantly depending on the green solvent used. These impurities had a considerable impact on the electrochemical performance of the recovered materials. TEP and PC yielded high-purity active materials and aluminum foils suitable for reuse or direct recycling, while Cyrene resulted in substantial residues of PVDF/solvent, requiring additional post-processing. Additionally, the recyclability of these green solvents was influenced by their solubility power for PVDF. This study provides valuable insights into the green solvent-based recycling process, laying the groundwork for future sustainable practices in LIB recycling.

Tuning crosslinking of hybrid preceramic polymers in vat photopolymerization toward controlled ceramic yields

Control of preceramic polymer crosslinking for UV-curable processing is essential for fine 3D printing with high ceramic conversion for sustainable polymer-derived ceramics (PDC) engineering. While various factors influencing ceramic yield have been studied, the systematic exploration of the relationship between crosslinking and ceramic yield, especially when crosslinking increases volatile elements, remains open for further investigation. This study addresses this gap by utilizing vat photopolymerization (VP) additive manufacturing (AM) as a versatile platform for controlling preceramic crosslinking and ceramic yield. By rationally designing and tuning the photochemical crosslinking through digital light processing (DLP), we demonstrate that the ceramic yield can be enhanced from 64% to over 86%, even with added volatile elements. We reveal that the post-pyrolysis ceramic yield can be closely correlated with the pre-pyrolysis crosslinking of the preceramic network represented by its stiffness. This correlation thereby suggests a fast, energy-efficient, non-destructive methodology to predict and improve ceramic yield. Combined with these findings, our one-pot thiol-ene hybridization of polycarbosilane and polycarbosiloxane via DLP offers an exemplary method to generate hybrid preceramic polymers with tailored material properties toward target applications, and potentially even higher ceramic yields. This study thus contributes to achieving better resource- and energy-efficient preceramic polymer and PDC processing routes toward sustainable, advanced organic–inorganic materials manufacturing.

Ultra-permeable silk-based polymeric membranes for vacuum-driven nanofiltration.

Nanofiltration (NF) membranes are commonly supplied in spiral-wound modules, resulting in numerous drawbacks for practical applications (e.g., high operating pressure/pressure drop/costs). Vacuum-driven NF could be a promising and low-cost alternative by utilizing simple components and operating under an ultra-low vacuum pressure (<1 bar). Nevertheless, existing commercial membranes are incapable of achieving practically relevant water flux in such a system. Herein, we fabricated a silk-based membrane with a crumpled and defect-free rejection layer, showing water permeance of 96.2 ± 10 L m-2 h-1 bar-1 and a Na2SO4 rejection of 96.0 ± 0.6% under cross-flow filtration mode. In a vacuum-driven system, the membrane demonstrates a water flux of 56.8 ± 7.1 L m-2 h-1 at a suction pressure of 0.9 bar and high removal rate against various contaminants. Through analysis, silk-based ultra-permeable membranes may offer close to 80% reduction in specific energy consumption and greenhouse gas emissions compared to a commercial benchmark, holding great promise for advancing a more energy-efficient and greener water treatment process and paving the avenue for practical application in real industrial settings.

Performance assessment of graphene oxide based nano-cutting fluids during sustainable hard turning through synthesis, characterization and machinability investigation

Achieving sustainability in manufacturing necessitates the implementation of eco-friendly solutions by adopting environmentally friendly coolants with cost-effective, energy-efficient and cleaner production processes without posing any risk to the operator's health. In this current work, mineral oil-based graphene oxide (GO) nano-cutting fluids of different concentration (0.05, 0.1, 0.2, 0.3, 0.4, 0.5 % wt.) are synthesized and their characteristics such as rheological, thermo-physical and tribological properties along with their stability and toxicology impact are thoroughly investigated. Further, the influence of nano-cutting fluid concentration on machinability and sustainability aspects during hard turning such as flank wear, surface integrity, power consumption, cutting temperature, carbon and noise emissions are investigated. According to the result an enhancement of 28.83 % and 29.54 % in viscosity and thermal conductivity have been recorded when concentration of nanofluid increases from 0.05 % to 0.5 %. GO nano-cutting fluids shows anti wear and friction reduction properties by providing excellent lubricious properties, as a result coefficient of friction has been reduced up to 28.36 % at 0.5 % wt. GO compared to plane mineral oil. A notable reduction in flank wear, cutting temperature, surface roughness, cutting temperature, power consumption, carbon emission and noise emission were noticed to be 25.96 %, 27.82 %, 23.86 %, 25.22 %, 11.66 % and 2.72 % respectively when the concentration of GO nanoparticles increases from 0.05 % to 0.5 % wt. Smoother surface with minimal defect has been noticed from the SEM and AFM image of machined workpiece at highest concentration (0.5 % wt.) of GO due to the synergetic effect of GO nano-cutting fluid and dual nozzle MQL system.

Experimental Research of the Drying Behavior of White Cabbage Leaves With Three Different Geometries in a Wind Energy‐Assisted Hybrid Hot Air Dryer

ABSTRACTIn this paper, the performance of a wind energy‐supported hybrid hot air dryer was examined experimentally. Wind energy provided overall electrical energy needed for the activation of the dryer. In the experiments, the drying behavior of white cabbage leaves with the same surface area and three different geometries: circle, rectangle, and square cross‐section was examined using three different constant temperatures (50°C, 60°C, and 70°C), and two different constant air speeds (0.5 and 1 m/s). The main purpose of using different geometries during the drying process was to determine the optimum cross‐section and working parameters for white cabbage leaves. The based aim of the study was to contribute to both increasing the energy efficiency of processes that cause high energy consumption, such as drying, and ensuring their continuity throughout the four seasons, by using a clean renewable energy source such as wind. Consequent the experiments, it was seen that the drying time decreased by approximately 52.63% and the energy consumption reduced by approximately 47.9% with the increase in temperature and air speed in rectangular‐sectioned cabbages compared to other geometries. In addition, in the longest‐term experiment, a maximum of 40% of the stored energy was used. This revealed that the developed hybrid system has approximately 52.5% higher energy efficiency than other drying units supporting with PV panels and also provides an average at the rate of 45% advantage in terms of product drying time.

Programmed Diffraction for Intelligent Imaging and Sensing

Advances in artificial intelligence have opened a powerful approach: compact optical networks of diffractive surfaces that can rapidly achieve specific tasks via direct, energy-efficient processing of light.

Green chemistry in manufacturing: Innovations in reducing environmental impact

Green chemistry has emerged as a transformative approach in reducing the environmental impact of the manufacturing industry. This review paper provides a comprehensive examination of how green chemistry principles are applied in various manufacturing sectors to minimize hazardous waste and energy consumption. The paper begins by outlining the core principles of green chemistry, highlighting their relevance to sustainable industrial practices. Key innovations in chemical engineering are discussed, focusing on waste reduction, energy-efficient processes, and the use of renewable raw materials. Through detailed case studies in the pharmaceutical, textile, and automotive industries, the paper demonstrates how the adoption of green chemistry has significantly reduced the environmental footprint of these sectors. The review also explores the challenges industries face in implementing green chemistry and the future direction of this field, emphasizing the role of policy and regulatory frameworks in driving further innovation. The findings suggest that while progress has been made, there are substantial opportunities for growth, especially in expanding the use of green chemistry across diverse industries. This paper concludes with recommendations for continued research and broader adoption of green chemistry to promote sustainable manufacturing.

Direct quantitative assessment of single-atom metal sites supported on powder catalysts

Rational design of effective catalyst demands a profound understanding of active-site structures. Single-atom supported powder catalysts, depicting unique features like enhanced metal dispersion, hold promise in different applications. Here, we present an approach to directly quantify the detailed structural nature of metal sites in single-atom, high surface area, powder catalysts. By combining advanced high-resolution scanning-transmission electron microscopy, deep learning and density functional theory calculations, we determine, with statistical significance, the exact location and coordination environment of Pd single-atoms supported on MgO nanoplates. Our findings reveal a preferential interaction of Pd single-atoms with cationic vacancies (V-centers), followed by occupation of anionic defects on the {001} MgO surface. The former interaction results in stabilization of PdO species within V-centers, while partially embedded Pd states are found in F-defects. This methodology opens a route to the ultimate structural analysis of metal-support interaction effects, key in the design of advanced nanocatalysts for sustainable and energy-efficient processes.

Dynamic Performance and Power Optimization with Heterogeneous Processing-in-Memory for AI Applications on Edge Devices

The rapid advancement of artificial intelligence (AI) technology, combined with the widespread proliferation of Internet of Things (IoT) devices, has significantly expanded the scope of AI applications, from data centers to edge devices. Running AI applications on edge devices requires a careful balance between data processing performance and energy efficiency. This challenge becomes even more critical when the computational load of applications dynamically changes over time, making it difficult to maintain optimal performance and energy efficiency simultaneously. To address these challenges, we propose a novel processing-in-memory (PIM) technology that dynamically optimizes performance and power consumption in response to real-time workload variations in AI applications. Our proposed solution consists of a new PIM architecture and an operational algorithm designed to maximize its effectiveness. The PIM architecture follows a well-established structure known for effectively handling data-centric tasks in AI applications. However, unlike conventional designs, it features a heterogeneous configuration of high-performance PIM (HP-PIM) modules and low-power PIM (LP-PIM) modules. This enables the system to dynamically adjust data processing based on varying computational load, optimizing energy efficiency according to the application’s workload demands. In addition, we present a data placement optimization algorithm to fully leverage the potential of the heterogeneous PIM architecture. This algorithm predicts changes in application workloads and optimally allocates data to the HP-PIM and LP-PIM modules, improving energy efficiency. To validate and evaluate the proposed technology, we implemented the PIM architecture and developed an embedded processor that integrates this architecture. We performed FPGA prototyping of the processor, and functional verification was successfully completed. Experimental results from running applications with varying workload demands on the prototype PIM processor demonstrate that the proposed technology achieves up to 29.54% energy savings.

Deploying large language models on diverse computing architectures: A performance evaluation framework

Deploying large language models (LLMs) across diverse computing architectures is a critical challenge in the field of artificial intelligence, particularly as these models become increasingly complex and resource-intensive. This review presents a performance evaluation framework designed to systematically assess the deployment of LLMs on various computing architectures, including CPUs, GPUs, TPUs, and specialized accelerators. The framework is structured around key performance metrics such as computational efficiency, latency, throughput, energy consumption, and scalability. It considers the trade-offs associated with different hardware configurations, optimizing the deployment to meet specific application requirements. The evaluation framework employs a multi-faceted approach, integrating both theoretical and empirical analyses to offer comprehensive insights into the performance dynamics of LLMs. This includes benchmarking LLMs under varying workloads, data batch sizes, and precision levels, enabling a nuanced understanding of how these factors influence model performance across different hardware environments. Additionally, the framework emphasizes the importance of model parallelism and distribution strategies, which are critical for efficiently scaling LLMs on high-performance computing clusters. A significant contribution of this framework is its ability to guide practitioners in selecting the optimal computing architecture for LLM deployment based on application-specific needs, such as low-latency inference for real-time applications or energy-efficient processing for large-scale deployments. The framework also provides insights into cost-performance trade-offs, offering guidance for balancing the financial implications of different deployment strategies with their performance benefits. Overall, this performance evaluation framework is a valuable tool for researchers and engineers, facilitating the efficient deployment of LLMs on diverse computing architectures. By offering a systematic approach to evaluating and optimizing LLM performance, the framework supports the ongoing development and application of these models across various domains. This paper will evaluate the deployment of large language models (LLMs) on diverse computing architectures, including x86, ARM, and RISC-V platforms. It will discuss strategies for optimizing LLM performance, such as dynamic frequency scaling, core scaling, and memory optimization. The research will contribute to understanding the best practices for deploying AI applications on different architectures, supporting technological innovation and competitiveness.

Impact of the feed particle size distribution and its packing characteristics on compression comminution

Due to its lower energy consumption than a conventional grinding circuit, there is an increased interest in the mining industry in the High-Pressure Grinding Rolls (HPGR) technology. Despite the benefits, the high quantity of material required to size the HPGR makes it difficult for new mines to consider this technology. The piston press test has been used at The University of British Columbia to predict the behavior of the HPGR. It is possible to predict energy requirements, size reduction, and throughput by utilizing a small amount of material to size the HPGR. The bulk material’s particle size distribution (PSD) plays an important role in how the particle bed will pack. This study investigates the differences in compressing three different sample PSDs obtained from the same copper ore crushed to −12.5 mm. The first PSD corresponds to the natural distribution resulting from the crusher. The second is created artificially to match the Fuller curve PSD, which theoretically should have the highest packing density. The third corresponds to a truncated feed produced by removing the fines (−300μm) from the natural feed. Tests were performed using a piston-and-die press test apparatus to compress the samples at different force levels. Entirely different behaviors are obtained while compressing the same material with different PSDs. Particularly, the Fuller curve PSD achieves a 31%–40% higher grinding rate than the natural feed, while the truncated feed achieves a 9%–30% higher grinding rate than the natural feed. Differences in the specific energy consumption, grinding efficiency, and final compacted densities highlight the critical influence of feed particle size distribution on the energy usage and generation of fine particles, underscoring the importance of optimizing these parameters for energy-efficient mineral processing. The overall findings indicate that an HPGR feed particle size distribution that matches the Fuller curve and, therefore, has a maximum packing density, results in the most energy-efficient comminution.

All-organic transistors printed on a biodegradable and bioderived substrate for sustainable bioelectronics

Biodegradable electronics is an incipient need in order to mitigate the alarming increase of electronic waste worldwide caused by capillary penetration of electronic devices and sensors. Flexibility, solution processability, low capital expenditure, and energy-efficient processes, which are distinctive features of organic printed electronics, have to be complemented by a sustainable sourcing and end-of-life of materials employed. This requirement calls for solutions where materials, especially substrates that typically represent the largest volume, can be biodegraded in the environment with no harm, yet assuring that no precious resources are dispersed. In this work, the bioderived and biodegradable biopolymer polyhydroxybutyrate (PHB) was used as a substrate, cast from an acetic acid solution, for all-organic field effect transistors (OFETs) based on an inkjet printed polymer semiconductor. The OFETs showed small device-to-device variation, a proper current modulation with ION/IOFF of about 1.2·103, mobility values as high as 0.07 cm2/Vs in saturation regime and channel length/width normalized leakage currents in the order of nA, which remained almost unaltered also after intensive mechanical stresses upon bending and rolling. Such mechanical stability and flexibility, together with the biodegradability and bioderivation, make PHB an appealing candidate for the development of sustainable printed bioelectronics, with widespread future applications in the biomedical and food packaging sector.

Application of Response Surface Methodology for the Optimization of Operating Conditions of a Self‐Pulsing Discharge (SPD) Plasma Reactor for the Degradation of Perfluorooctanoic Acid (PFOA) in Water

ABSTRACTThis study explores atmospheric plasma as a novel approach for the degradation of persistent per‐ and polyfluoroalkyl substances (PFAS) in contaminated waters. Using response surface methodology and Box–Behnken design, the performance of the self‐pulsing discharge (SPD) reactor was optimized by adjusting the following independent factors: input power, plasma area‐to‐liquid volume ratio, and argon bubbling time. Optimization was assessed using four specific indicators: kPFOA and G50, for the process velocity and energy efficiency, respectively; kPFOA/kPFHpA and ΣPFAS/C0, both for the presence of PFAS in the treated water for the process products. Under the optimized operating conditions, residual PFAS summed up to only 2.4% of the carbon initially present as PFOA, and a remarkable G50 value of (523 ± 10) mg/kWh was obtained.

Enhancing catalytic pyrolysis of polypropylene using mesopore-modified HZSM-5 catalysts: insights and strategies for improved performance

Designing an active, selective, and stable catalyst for catalytic polyolefin pyrolysis is crucial for enhancing energy efficiency and economic viability in chemical processes. In this study, two synthesis methods—NaOH and NaOH/CTAB treatments—were employed to modify the physicochemical properties of CBV23, CBV55, and CBV80 zeolites. The catalytic performance of both parent and modified zeolites was evaluated for polypropylene pyrolysis using a two-stage micro-pyrolyzer coupled with two-dimensional GC-FID/MS. The NaOH/CTAB treatment preserved and enhanced strong acid sites while promoting a more uniform mesopore distribution. Among the catalysts tested, the hierarchical CBV80-ZM exhibited the best performance, achieving a propylene yield of 41 wt% and total light olefin and MA yields of 92 wt%. The improved catalytic performance was attributed to optimized acidity and larger pore size, which reduced the number of weak acid sites. These findings offer valuable insights for designing tailored zeolites based on specific target products for catalytic pyrolysis of plastic waste, particularly in the production of propylene and other high-value chemicals.

Bionic firing activities in a dual mem-elements based CNN cell

Firing activities provide the potential possibility for achieving bio-brain functionality with high energy-efficient and high-speed information processing performance. This inspires the design of bionic circuits to generate firing activities and develop brain-like applications. In this paper, a dual mem-elements based cellular neural network (CNN) cell is constructed to produce bionic firing activities, in which a non-ideal memcapacitor and an N-type locally active memristor are employed to emulate the functions of the neuronal membrane. The proposed CNN cell has an excitation-dependent equilibrium trajectory and stability. Numerical analysis shows that the dual mem-elements based CNN cell has abundant dynamical behaviors of forward/reverse period-doubling bifurcation routes, chaos crisis, tangent bifurcation, and bubbles with the change of model parameters of the CNN cell, memcapacitor, and exciting source. As a result, the rich firing patterns’ transition can be observed from the two-dimensional dynamics evolution. The analog circuit of the proposed CNN cell is designed, and then a PCB-based hardware circuit is implemented. The experimental results certify the accuracy of the theoretical and numerical analysis.

A global fairtrade partnership needed to address injustices in the supply chains of clean energy technology materials

Abstract Renewable sources produced close to one-third of the world’s electricity in 2023. However, a limited but growing body of research suggests rapid renewable energy development is leading to conflict and resource exploitation in energy-transitioning communities. Such injustices are attributable to the extractivist nature of renewable energy development, where raw materials, also known as Clean Energy Technology Materials (CETMs), are in limited quantities and often concentrated in resource-constrained zones in the Global South. In this perspective, we call for an urgent need for energy justice considerations in CETM’s supply chain. We used demand projection data from 2020 to 2040 to look into the effects of important CETMs like nickel, cobalt, and lithium on distributive justice. We also examined the potential of these effects to tackle systemic injustices such as conflict, labor exploitation, and transactional colonialism. Next, we analyzed global mining production data from the United States Geological Survey using a CETM life cycle lens and found that increasing demand for these materials is exacerbating restorative injustices, particularly in the Global South. Finally, building on the above evidence, we called for the creation of multi-stakeholder partnerships and the establishment of fair trade standards across the critical CETM supply chain. Graphical abstract Highlights Here, we analyzed the projected demand growth for selected clean energy technology materials by 2040 relative to 2020 levels using data from the International Energy Agency, visualized their global mining production using data from the United States Geological Survey, explained how the demand for these materials is exacerbating certain injustices, and recommended multi-stakeholder partnerships across the supply chain of these materials. Discussion The rapid growth of renewable energy technologies is creating injustices throughout the supply chain of clean energy technology materials (CETM). A lack of any energy justice framework across CETMs’ extraction, processing, decommissioning, and recycling is exacerbating restorative injustices, especially in the Global South. By examining the projected demands and geospatial patterns for the extraction of minerals, metals, and other materials essential for clean energy technology development, the inequities faced by impoverished, marginalized, and Indigenous communities become apparent. We argue that if coffee can have fair trade standards across its supply chain, why can’t we have similar considerations for the CETMs? There is a need to include transparency in the sustainability, ethics, and energy efficiency of CETM extraction and processing through global partnerships across its supply chain.

Coherent spin wave excitation with radio-frequency spin–orbit torque

Spin waves, collective perturbations of magnetic moments, are both fundamental probes for magnetic physics and promising candidates for energy-efficient signal processing and computation. Traditionally, coherent propagating spin waves have been generated by radio frequency (RF) inductive Oersted fields from current-carrying electrodes. An alternative mechanism, spin–orbit torque (SOT), offers more localized excitation through interfacial spin accumulation but has been mostly limited to DC to kHz frequencies. SOT driven by RF currents, with potentially enhanced pumping efficiency and unique spin dynamics, remains largely unexplored, especially in magnetic insulators. Here, we conduct a comprehensive theoretical and computational investigation into the generation of coherent spin waves via RF-SOT in the prototypical yttrium iron garnet. We characterize the excitation of forward volume, backward volume, and surface modes in both linear and nonlinear regimes, employing single and interdigitated electrode configurations. We reveal and explain several unique and surprising features of RF-SOT compared to inductive excitation, including higher efficiency, distinct mode selectivity, and directional symmetry, a ∼3π/4 phase offset, reduced anharmonic distortion in the nonlinear regime, and the absence of second harmonic generation. These insights position RF-SOT as a promising new mechanism for future magnonic and spintronic applications.

Fabrication of Active Z-Scheme Sr2MgSi2O7: Eu2+, Dy3+/COF Photocatalyst for Round-the-Clock Efficient Removal of Total Cr.

Photoreduction is recognized as a desirable treatment method for hexavalent chromium (Cr(VI)). However, it has been limited by the intermittent solar flux and limited light absorption. In this work, a novel Z-scheme photocatalyst combining a covalent organic framework (COF) with Eu2+, Dy3+ co-doped Sr2MgSi2O7 (Sr2MgSi2O7:Eu2+, Dy3+) is synthesized, which shows the high spectral conversion efficiency and works efficiently in both light irradiation and dark for Cr(VI) reduction. Sr2MgSi2O7:Eu2+, Dy3+ serves as both an electron transfer station and active sites for COF molecule activation, thus resulting in 100% photoreduction of Cr(VI) (50 mL, 10 mg/L) with high light stability and over 1 h dark activity. Moreover, the XPS and FT-IR analyses reveal the existence of functional groups (Si-OH on Sr2MgSi2O7:Eu2+, Dy3+, and -NH- on COFTP-TTA) on the composited catalyst as active sites to adsorb the resultant Cr(III) species, demonstrating a synergistic effect for total Cr removal. This work provides an alternative method for the design of a round-the-clock photocatalyst for Cr(VI) reduction, allowing a versatile solid surface activation for establishing a more energy efficient and robust photocatalysis process for Cr pollution cleaning.