Abstract This study utilized siderite tailings and polypropylene as the main raw materials to prepare stone paper composites through the melt blending and hot-pressing molding process. Using characterization techniques such as scanning electron microscopy and a universal electronic testing machine, the effects of the addition amounts of four different additives – calcium stearate, maleic anhydride grafted polypropylene, silane coupling agent KH550, and polypropylene toughening agent – on the microstructure, tightness, tensile strength, smoothness, and tear resistance of the materials were systematically investigated under a fixed tailings content of 40%. The results demonstrated that an appropriate number of additives effectively improved the overall performance of the materials, whereas excessive addition not only increased costs but also potentially led to a decline in performance. In summary, the rational use of additives significantly enhanced the compatibility and dispersion between the tailings and the polypropylene matrix. Under the specific conditions of 40 wt% siderite tailings loading and the processing parameters used in this study, the optimal addition amounts for calcium stearate, maleic anhydride grafted polypropylene, KH550, and the polypropylene toughening agent were found to be 2, 2, 2, and 5%, respectively.

With the advancement of industrial production and urban modernization, pollution from heavy metal ions and the accumulation of solid waste have become critical global environmental challenges. Establishing an effective recycling system for solid waste and removing heavy metals from wastewater is essential. Coal gangue was used in this study as the primary material for the synthesis of a fully coal gangue-based phosphorus-silicon-aluminum (SAPO-5) molecular sieve through a hydrothermal process. The SAPO-5 molecular sieve was characterized through several methods, including X-ray diffraction (XRD), scanning electron microscopy (SEM), BET surface analysis, Fourier-transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS), to examine its mineral phases, microstructure, pore characteristics, and material structure. Adsorption performance towards wastewater with Cd2+ and Pb2+ ions was investigated. It was found that the adsorption processes of these ions are well described by both the pseudo-second-order model and the Langmuir isotherm. According to the Langmuir model, the coal gangue-based SAPO-5 molecular sieve exhibited maximum adsorption capacities of 93.63 mg·g-1 for Cd2+ and 157.73 mg·g-1 for Pb2+. After five cycles, the SAPO-5 molecular sieve retained strong stability in adsorbing Cd2+ and Pb2+, with residual adsorption capacities of 77.03 mg·g-1 for Cd2+ and 138.21 mg·g-1 for Pb2+. The excellent adsorption performance of the fully solid waste coal gangue-based SAPO-5 molecular sieve is mainly attributed to its mesoporous channel effects, the complexation of -OH functional groups, and electrostatic attraction.

The accumulation of organic pollutants and solid waste is one of the major environmental challenges faced globally. Establishing an efficient recycling system for solid waste and designing cost-effective, high-performance photocatalysts are urgent tasks for the removal of organic pollutants from water. This study utilizes coal gangue as the precursor to synthesize a coal gangue-based phosphorus-silicon-aluminum molecular sieve (SAPO-5) via hydrothermal synthesis. The resulting material was then composited with bismuth oxybromide (BiOBr) to form a novel BiOBr/coal gangue-based SAPO-5 nanocomposite. When the mass ratio of BiOBr to coal gangue-based SAPO-5 molecular sieve is 0.3, the synthesized nanocomposite exhibits excellent adsorption and photocatalytic performance for the removal of methylene blue, achieving a removal rate of 97.8% and the mineralization rate of 57.4% within 30 min. The superior performance can be attributed to the optimal pore size, rapid charge transfer rate, and high photogenerated charge density of the BiOBr/coal gangue-based SAPO-5 nanocomposite. The novel BiOBr/coal gangue-based SAPO-5 molecular sieve nanocomposite catalyst presents a new approach for the harmless treatment of organic dye wastewater and the high-value utilization of coal gangue.

Lithium–sulfur batteries (LSBs) are gaining much attention because they offer a much higher theoretical energy density compared to traditional lithium-ion batteries. However, the cycling performance of LSBs with high sulfur mass loading is poor due to the shuttle effect, limiting the practical application of LSBs. In this work, a unique porous sulfur/Ti3C2Tx Mxene@selenium (S/Ti3C2Tx@Se) cathode of a LSB is synthesized by a simple hydrothermal method to address these challenges. In this composite, Ti3C2Tx forms a conductive framework and Se is tightly anchored on the framework. The Se inhibits the agglomeration of Ti3C2Tx and prevents the collapse of Ti3C2Tx. The S/Ti3C2Tx@Se composite can adsorb lithium polysulfides (LiPSs) and suppresses the shuttle effect and volume changes during cycling, improving the cycling stability of LSBs with high S loading. A high capacity of 812.2 mAh g−1 at 0.1 C with 5.0 mg cm−2 sulfur mass loading after 100 cycles is obtained. This work could inspire further research into high-performance S host materials for high-S-loading LSBs.

Capacity estimation plays a crucial role in battery management systems, and is essential for ensuring the safety and reliability of lithium-sulfur (Li-S) batteries. This paper proposes a method that uses a long short-term memory (LSTM) neural network to estimate the state of health (SOH) of Li-S batteries. The method uses health features extracted from the charging curve and incremental capacity analysis (ICA) as input for the LSTM network. To enhance the robustness and accuracy of the network, the Adam algorithm is employed to optimize specific hyperparameters. Experimental data from three different groups of batteries with varying nominal capacities are used to validate the proposed method. The results demonstrate the effectiveness of the method in accurately estimating the capacity degradation of all three batteries. Also, the study examines the impact of different lengths of network training sets on capacity estimation. The results reveal that the ICA-LSTM model achieves a prediction accuracy of mean absolute error 4.6% and mean squared error 0.21% with three different training set lengths of 20%, 40%, and 60%. The analysis demonstrates that the lightweight model maintains high SOH estimation accuracy even with a small training set, and exhibits strong adaptive and generalization capabilities when applied to different Li-S batteries. Overall, the proposed method, supported by experimental validation and analysis, demonstrates its efficacy in ensuring accurate and reliable SOH estimation, thereby enhancing the safety and performance of Li-S batteries.

Abstract In order to improve the flame retardant properties of wood plywood, intumescent flame retardant coatings were prepared using melamine, borate, pentaerythritol and urea as the basic formulations and gangue as a modified additive. The flame retardancy of the samples was characterized using a cone calorimeter, scanning electron microscope, X-ray diffraction and thermogravimetric analysis. In addition, the study investigated the effect of gangue content on the properties of the prepared coatings. The results show that the doping of gangue in intumescent flame retardant coatings can improve the flame retardant effect of the coatings. Specifically, when the mass fraction of gangue was 8 wt%, the exothermic rate, total smoke production and total exothermic amount of the coating were significantly reduced. Moreover, the addition of gangue promoted the formation of a continuous and dense carbon layer structure during the combustion process of the coating, which produced a molten substance that effectively isolated oxygen and heat, thus strengthening the fire-retardant and heat-insulating properties of the coating. The results of this study provide valuable insights into the development of flame retardant coating formulations for wood plywood.

Designing bifunctional oxygen reduction/evolution (ORR/OER) catalysts with high activity, robust stability and low cost is the key to accelerating the commercialization of rechargeable zinc-air battery (RZAB). Here, we propose a template-assisted electrospinning strategy to in situ fabricate 3D fibers consisting of FeNi nanoparticles embedded into N-doped hollow porous carbon nanospheres (FeNi@NHCFs) as the stable binder-free integrated air cathode in RZAB. 3D interconnected conductive fiber networks provide fast electron transfer pathways and strengthen the mechanical flexibility. Meanwhile, N-doped hollow porous carbon nanospheres not only evenly confine FeNi nanoparticles to provide sufficient catalytic active sites, but also endow optimum mass transfer environment to reduce diffusion barrier. The RZABs assembled by FeNi@NHCFs as integrated air cathodes exhibit outstanding battery performance with high open-circuit voltage, large discharge specific capacity and power density, durable cyclic stability and great flexibility. Thus, this work brings a useful strategy to fabricate the integrated electrodes without using any polymeric binders for metal air batteries and other related fields.

Fast-charging technology is the inevitable trend for electric vehicles (EVs). Current EVs’ lithium-ion batteries (LIBs) cannot provide ultrafast power input due to the capacity fading and safety hazards of graphite anode at high rates. Lithium vanadate oxide (Li3VO4) has been widely studied as fast-charging anode material due to its high capacity and stability at high rates. However, its highly safe characteristic under fast-charging has not been studied. In this study, a fast-charging anode material is synthesized by inserting Li3VO4 in Ti3C2Tx MXene framework. The morphologies of Li3VO4/Ti3C2Tx electrode after cycling at different rates were studied to analyze the dendrites growth. Electrochemical testing results demonstrate that Li3VO4/Ti3C2Tx composite displays high capacities of 151.6 mA h g−1 at 5 C and 87.8 mA h g−1 at 10 C, which are much higher than that of commercial graphite anode (51.9 mA h g−1 at 5 C and 17.0 mA h g−1 at 10 C). Moreover, Li3VO4/Ti3C2Tx electrode does not generate Li dendrite at high rates (5 and 10 C) while commercial graphite electrode grows many Li dendrites under the same conditions, demonstrating fast-charging and high safety of Li3VO4/Ti3C2Tx composite. Our work inspires promising fast-charging anode material design for LIBs.

Safety and reliability are crucial for the next-generation supercapacitors used in energy storage systems, while accurate prediction of the degradation trajectory and remaining useful life (RUL) is essential for analyzing degradation and evaluating performance in energy storage systems. This study proposes a novel data processing and improved one-dimensional convolutional neural network (1D CNN)-informer framework for robust RUL prediction. In data preprocessing, all data from two structures are adjusted to a unified format, and cross-entropy loss is used to couple the 1D CNN and informer. Then, the minimum-maximum feature scaling method is used for normalization to accelerate the training process in reaching the minimum cost function. A relative position encoding algorithm is introduced to improve the Informer model, enabling it to better learn the sequence relationships between data and effectively reduce prediction variability. Supercapacitor data in different working conditions are used to validate the proposed method. Compared with other existing methods, the maximum root mean square error is reduced by 32.71%, the mean absolute error is reduced by 28.50%, and R 2 is increased by 4.79%. The strategy considers the complementarity between two single models, which can extract features and enrich local details, as well as enhance the model's global perception ability. The experimental results demonstrate that the proposed model achieves high-precision and robust RUL prediction, thereby promoting the industrial application of supercapacitors.

Using coal gangue (CG) as raw material, a new type of all solid-waste-based 13-X molecular sieve material was controllably prepared by alkali fusion-hydrothermal method. The synthetic molecular sieve was used as a solid adsorbent to treat Cd2+-containing wastewater, and its adsorption behavior on Cd2+ in aqueous solution was studied and analyzed. The microstructure and morphology of the molecular sieve were investigated by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and specific surface area analyzer. The results show that the synthesized 13-X molecular sieve has higher Brunauer-Emmett-Teller (BET) specific surface area with higher crystallinity and higher adsorption capacity for the heavy metal Cd2+. The adsorption process of Cd2+ by molecular sieve conforms to the Langmuir isotherm adsorption equation and Lagergren pseudo-second-order rate equation. Combined with thermodynamic calculation, it can be concluded that the adsorption process is physically monolayer, spontaneous and exothermic. In this study, a low-cost and naturally available synthesis method of 13-X molecular sieve is reported. Combined with its adsorption mechanism for Cd2+, it provides a feasible and general method for removing heavy metal ions from coal gangue and also provides a new way for the utilization of coal gangue with high added value.

This paper proposes a heterogeneous substrate that can be applied to flexible antennas. The substrate is fabricated using PolyJet 3D printer. Heterogeneous substrates are composed of digital ABSs which are evenly distributed in the flexible material Agilus. By changing the volume ratio of the digital ABS, the mechanical and dielectric properties of the heterogeneous substrate can be adjusted. Firstly, the dielectric properties of the heterogeneous substrate are estimated by the effective medium approximation theory, and the dimensions of the heterogeneous substrate and the digital ABSs with different volume ratio are designed. Then,the hybrid printing is carried out using PolyJet according to the designed model, and the dielectric properties of the heterogeneous substrate are characterized through the ring resonator theory. Finally, a patch antenna is fabricated on the heterogeneous substrate and the performance of the antenna is tested under different bending conditions.

The utilization of steel slag with high added value is an important means for sustainable development of the iron and steel industry and environment. A new nano-scaled V/CeO2- functionalized steel-slag-based catalyst (V/CeO2-SC) was prepared. The XPS results revealed that V was embedded in the CeO2 lattice with V4 thorn and V5 thorn forms. The regression equation analysis of variance told that the order of influencing factors on malachite green dye degradation rate is: catalyst dosage > V doping amount > malachite green dye con-centration. The optimum values of the parameters were dye concentration 10 mg/L, catalyst dosage 0.1 g, and V doping amount 1wt%. Under the optimal condition, the pre-dicted and actual response values of the degradation rate of malachite green dye were 100.00%. UV-Visible light absorption spectroscopy results demonstrated that after the photocatalytic degradation reaction, the absorption peak of the malachite green dye so-lution almost completely disappeared, indicating that the malachite green dye solution was almost completely degraded. The excellent degradation activity of the 1V/CeO2-SC specimen was attributed to the co-action of the high SBET, large mesoporous volume, and the coupled semiconductors formed by V-doped CeO2 and FeO in the catalyst carrier. A possible photocatalytic degradation mechanism of the V/CeO2-SC specimen was proposed.(c) 2023 The Authors. Published by Elsevier B.V.

膨胀型阻燃剂是阻燃性能优异的无卤阻燃剂,在阻燃过程中具备低烟、低毒、防火效率高等特性,是目前阻燃剂研究的1个重要方向.文章介绍了膨胀型阻燃剂的不同分类与组成以及膨胀型阻燃剂阻燃机理,总结了现有的膨胀型阻燃剂的研究进展,重点从膨胀型阻燃剂表面改性、单成分膨胀型阻燃剂开发、阻燃协同剂的使用和新型膨胀型阻燃剂组分等方面进行阐述和总结,指出膨胀型阻燃剂发展需要解决的问题.未来膨胀型阻燃剂应该向绿色化、环保化方向发展.

Porous lead-free piezoelectric ceramics are characterized by their environment-friendly, light weight, and large specific surface area. The optimization of porous Na0.5Bi0.5TiO3-based lead-free piezoelectric ceramics can improve piezoelectric properties, enhance force–electric coupling characteristics, and effectively promote energy conversion, expanding the application in force-electric coupling devices. This study aimed to prepare [Smx(Bi0.5Na0.5)1−3x/2]0.94Ba0.06TiO3 (x = 0, 0.01, 0.02, 0.03, 0.04) lead-free ceramics with porous structures, resulting in the piezoelectric constant d33 = 131 pC/N and the plane electromechanical coupling coefficient kp = 0.213 at x = 0.01. The presence of pores in lead-free ceramics has a direct impact on the domain structure and can cause the depolarization process to relax. Then, the soft doping of Sm3+ makes the A-site ion in porous (Bi0.5Na0.5)0.94Ba0.06TiO3 ceramics occupancy inhomogeneous and generates cation vacancies, which induces lattice distortion and makes the domain wall motion easier, resulting in the improvement of piezoelectric properties and electromechanical coupling parameters. Furthermore, the piezoelectric oscillator exhibits greater resistance to resonant coupling in the radial extension vibration mode. These results infer that a combination of porosity and Sm3+ doping renders (Bi0.5Na0.5)0.94Ba0.06TiO3 ceramics base material for piezoelectric resonators, providing a scientific basis for their application in force–electric coupling devices, such as piezoelectric resonant gas sensors.

The large accumulation of coal gangue, a common industrial solid waste, causes severe environmental problems, and green development strategies are required to transform this waste into high-value-added products. In this study, low-cost ceramsites adsorbents were prepared from waste gangue, silt coal, and peanut shells and applied to remove the organic dye methylene blue from wastewater. We investigated the microstructure of ceramsites and the effects of the sintering atmosphere, sintering temperature, and solution pH on their adsorption performance. The ceramsites sintered at 800°C under a nitrogen atmosphere exhibited the largest three-dimensional-interconnected hierarchical porous structure among the prepared ceramsites; further, it exhibited the highest methylene blue adsorption performance, with an adsorption capacity of 0.954 mg·g−1, adsorption efficiency of over 95%, and adsorption equilibrium time of 1 h at a solution pH of 9. The removal efficiency remained greater than 75% after five adsorption cycles. The adsorption kinetics data were analyzed using various models, including the pseudo-second-order kinetic model and Langmuir equation, and the adsorption was attributed to electrostatic interactions between the dyes and ceramsites, n-interactions, and hydrogen bonds. The prepared coal gangue ceramsites exhibited excellent adsorption capacities, removal rates, and cyclic stabilities, demonstrating their promising application prospects for the comprehensive utilization of solid waste and for wastewater treatment.

Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in several application scenario. Accurate state of health (SOH) forecast is essential for designing a safe and reliable battery management systems (BMS). Temporal convolutional network (TCN) is a prevailing deep learning method for estimating the SOH of lithium-ion batteries. However, the hyperparameters in the network are usually difficult to predefine, which poses a challenge for the SOH estimation accuracy in real-world. To solve this problem, this paper pro-poses a data-driven estimation approach, where the TCN is combined with the modified flower pollination al-gorithm (MFPA) to determine the network topology. After hyperparameter optimization, the external sensor raw data and identified ohmic resistances trajectories in the equivalent circuits model (ECM) are both input to the TCN model to estimate SOH of LIBs. In contrast to prior approaches for feature extraction, this method is not only conductive to improve SOH estimation accuracy, but also can reduce on-board estimation computing burden. We carry out experiments on the same type of cells from NASA public data resources. The experimental results systematically validate the superiority of the proposed method, which covers high estimation accuracy, great robustness to varied training set and satisfied universality to different batteries.

Carbon dioxide emissions are the primary and most direct contributor to global warming, posing a significant hazard to both the environment and human health. In response to this challenge, there has been a growing interest in the development of effective carbon capture technologies. This study involved the synthesis of 13-X molecular sieve porous materials using solid waste coal gangue as a source of silicon and aluminum. The synthesis process involved the controlled utilization of an “alkali fusion-hydrothermal” reaction system. The resulting materials were characterized for their structure, morphology, and crystal composition using X-ray diffraction and field emission scanning electron microscopy. These 13-X molecular sieve materials were employed as adsorbents to capture carbon dioxide gas, and their adsorption performance was investigated. The findings indicated that the 13-X molecular sieve materials possess uniform pores and complete crystalline morphologies, and they exhibited an adsorption capacity of 1.82 mmol/g for carbon dioxide at 0 °C. Consequently, this study not only converted solid waste gangue into high-value products but also demonstrated effective atmospheric carbon dioxide capture, suggesting that gangue-based 13-X molecular sieves may serve as a potential candidate for carbon capture.

Nanoscale hierarchically porous metal-organic frameworks (NHP-MOFs) have received unprecedented attention in many fields owing to their integration of the strengths of nanoscale size (<1 mu m) and hierarchical porous structure (micro-, meso-and/or macro-pores) of MOFs. This review focuses on recent advances in the main synthetic strategies for NHP-MOFs based on different metal ions (e.g., Cu, Fe, Co, Zn, Al, Zr, and Cr), including the template method, composite technology, post-synthetic modification, in situ growth and the grind method. In addition, the mechanisms of synthesis, regulation techniques and the advantages and disadvantages of various methods are discussed. Finally, the challenges and prospects of the commercialisation of promising NHP-MOFs are also presented. The purpose of this review is to provide a road map for future design and development of NHP-MOFs for practical application.

Ni60 self-lubricated anti-wear composite coatings were successfully precipitated on the 35CrMoV substrate by laser cladding technology. The effects of heat treatment on the macro-morphology, microstructure, precipitated phase, microhardness, and wear properties of the composite coatings with different heat treatment temperatures (25 °C, 500 °C, 600 °C, and 700 °C for 1 h) were investigated systemically. The macro-morphology, microstructure, precipitated phases, and elements distribution of laser cladding layers were detected by optical microscopy (OM), scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS), respectively. The mechanical and tribological properties of the cladding layers were tested using a microscopic Vickers hardness tester and friction and wear tester, respectively. The results show that the main phases of Ni60 coatings are composed of γ-(Ni, Fe), Cr7C3, Cr23C6, CrB, CrFeB, and Cr2Ni3. In particular, the micro-structure and mechanical properties reach the best levels after heat treatment at 600 °C. The micro-hardness, average friction coefficient, and wear volume of the cladding layer are 771.4 to 915.8 HV1 and 0.434 and 2.9546 × 10−5 mm3, respectively. In conclusion, the micro-structure and mechanical properties of the cladding layer are greatly improved by the proper heat treatment temperature.

Lithium-ion batteries have become the fastest-growing energy storage equipment available for extrinsic and intrinsic reasons. State of Charge (SOC) is one of the lithium-ion batteries' most critical performance indicators, reflecting the remaining capacity. An accurate and stable estimate of SOC is critical for any lithium-ion battery. This paper proposes a hybrid method to achieve stable and real-time battery SOC estimation at different temperatures, composed of an Improved Bidirectional Gated Recurrent Unit (IBGRU) network and Unscented Kalman filtering (UKF). The proposed method is experimentally validated using data from UDDS and US06 driving cycles. The verification results show that the method can adapt to various working conditions and obtain good estimation accuracy and robustness, with MAE and RMSE less than 0.83% and 1.12%, respectively. After transfer learning, the method can also be applied to new lithium-ion batteries and achieve good estimation performance at new temperature conditions. The maximum errors are 4.98% and 5.76% at 25 degrees C and -10 degrees C, respectively. Therefore, the IBGRU-UKF method can achieve a more accurate and stable SOC estimation with good expansion performance for different lithium-ion batteries.