Search Results (539)

Nickel-rich NMC-811 is a benchmark cathode material for high-energy density lithium-ion batteries due to its high specific capacity (>200 mAh g⁻¹) and operating voltage (~3.8 V). However, its strong surface reactivity toward atmospheric species, particularly moisture and CO₂, poses significant challenges during storage and processing, leading to the formation of LiOH- and Li₂CO₃-rich surface layers. Although the effects of humid air have been widely investigated, a direct comparison between high relative humidity and pure CO₂ exposure remains limited. Here, we systematically examine the morphological, structural, chemical, and electrochemical evolution of commercial NMC-811 electrodes after 5 h exposure to 80% relative humidity or CO₂-saturated atmosphere. Moisture treatment induces substantial surface reconstruction, lattice shrinkage, and increased cation disorder, accompanied by extensive hydroxide and carbonate formation. In contrast, CO₂ exposure mainly modifies the outermost surface layer without significant bulk structural changes. Electrochemical testing reveals that CO₂-treated electrodes display higher initial polarization but quickly recover near-pristine performance, whereas humidity-treated electrodes exhibit persistent kinetic limitations, accelerated capacity fading, and earlier end-of-life. Overall, degradation severity follows the trend: pristine < CO₂ < RH 80%, highlighting the dominant role of moisture in irreversible structural deterioration. Full article

Operando mechanical behavior of lithium-ion batteries under aggressive conditions remains insufficiently quantified, especially under combined high-rate and deep-discharge operation. This study investigated strain evolution in a commercial 18650 NMC lithium-ion cell using surface-mounted fiber Bragg grating sensors across 20 sequential conditions combining five discharge rates (1–4.5 C) and four cutoff voltages (2.5–1.0 V). All tests were performed on a single cell using identical 0.5 C constant-current constant-voltage charging, followed by a 2 h rest period and controlled discharge, to systematically evaluate mechanochemical evolution with increasing electrochemical severity. Maximum tensile strain during charging ranged from 45 to 59 µε and showed limited sensitivity to discharge severity. In contrast, discharge behavior exhibited clear rate- and cutoff-dependent transitions from tensile to compressive deformation; the most severe condition (4.5 C, 1.0 V cutoff) produced a peak compressive strain of about −27 µε and the most negative residual strain after relaxation. Although temperature increased monotonically with C-rate, strain evolution was nonlinear and non-monotonic, indicating that electrochemically induced stress dominated over thermal expansion alone. These findings reveal progressive amplification of irreversible deformation under severe discharge and demonstrate the value of fiber Bragg grating sensing for operando assessment of electrochemical–mechanical coupling in cylindrical lithium-ion cells. Full article

Electric vehicles are becoming more common daily because countries are moving towards net-zero emissions. Different generations of NMC battery cells are used for EV applications. This work investigates the degradation behavior of high-energy 75 Ah prismatic NMC631 lithium-ion cells using a combined incremental capacity analysis (ICA) and electrochemical impedance spectroscopy (EIS) framework under different conditions. Cells are cycled at an identical C-rates and depths of discharge (DoD), and at different temperatures to systematically evaluate the impact of temperature on electrochemical aging. ICA results revealed that cells cycled at low temperatures maintain stable peaks and a high SoH (>90%) after completing 1600 full equivalent cycles (FECs). EIS analysis confirms the distinct impedance evolution patterns. Degradation mode analysis is performed using the ICA, and EIS highlights the combined evolution of conductivity loss, loss of lithium inventory, and loss of active material. It also highlights different degradation path trajectories under identical operating conditions stem from the progressive amplification of internal cell heterogeneities during aging. The results demonstrate that combining ICA and EIS provides complementary insights into degradation evolution and enables clear differentiation between gradual aging and sudden failure pathways in high-energy NMC cells. Full article

Carbapenems comprise a class of β-lactam antibiotics with broad-spectrum hydrolytic activity and are often reserved as last-line agents for the treatment of serious multidrug-resistant (MDR) bacterial infections. Clinically important nosocomial MDR Gram-negative bacteria (GNB) include Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Carbapenem resistance among these organisms is predominantly mediated by the production of β-lactamases called carbapenemases, such as K. pneumoniae carbapenemase (KPC), New Delhi metallo-β-lactamase (NDM), imipenemase (IMP), Verona integron-encoded metallo-β-lactamase (VIM), and selected oxacillinase (OXA)-type carbapenemases. These enzymes degrade carbapenems, significantly compromising their clinical efficacy. To address escalating antimicrobial resistance, novel next-generation β-lactamase inhibitors (BLIs), partnered with established β-lactams (BLs), have been approved or are currently under development to inhibit carbapenemase activity. The present narrative review aims to synthesize the most current information on the major carbapenemases and discusses recently approved and investigational BL/BLI combination therapies in terms of their mechanisms of action, spectrum of activity, gaps in coverage, and available clinical and in vitro evidence. Development of resistance to novel BL/BLI combinations is also examined. Comparative analysis of inhibitory spectra and microbiological coverage indicates a continued need for metallo-β-lactamase inhibitors with direct pan-inhibitory activity, pathogen-specific BL/BLI regimens for carbapenem-resistant A. baumannii, and carbapenemase-targeted agents effective in the context of non-enzymatic resistance mechanisms. Treatment-emergent resistance to novel BL/BLIs and limitations in activity profiles underscore the critical need for continued innovation in pipeline development, vigilant global and local surveillance of carbapenemase epidemiology, and robust antimicrobial stewardship strategies to aid in preserving the efficacy of the antibacterial drug armamentarium. Full article

Accurate and robust battery health prognostics are critical for reliable battery management in electronic devices and electric vehicles. Previous studies have demonstrated that combining electrochemical impedance spectroscopy (EIS) with machine learning enables accurate health-state forecasting in LiCoO₂ coin cells. However, the applicability of this EIS-AI paradigm across diverse chemistries and industrial-grade battery formats remains unvalidated, limiting its practical deployment in energy storage systems. Here, we develop an EIS–AI battery prognostic framework and validate its performance on LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) cylindrical cells and LiNi_0.8Mn_0.1Co_0.1O₂ (NMC811) pouch cells. A hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture is developed to estimate state of health (SoH) and predict remaining useful life (RUL) from EIS spectra. Trained on an in-house dataset comprising over 13,000 impedance spectra from 22 cells (8 NMC111 and 14 NMC811), the model achieves robust performance, with average coefficients of determination ($R^2$) exceeding 0.92 for SoH estimation and 0.90 for RUL prediction across various batteries and cycling protocols. Salient feature analysis further reveals chemistry- and protocol-dependent frequency regimes associated with degradation. These results demonstrate that impedance spectra constitute physically informative descriptors for data-driven battery prognostics and provide a scalable and interpretable pathway for deploying EIS-AI frameworks in real-world battery management systems (BMSs). Full article

This study aims to show a detailed life cycle assessment (LCA) approach of battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), with an emphasis on determining the electrical carbon intensity at which these vehicles reach life-cycle greenhouse gas (GHG) parity. The analysis was conducted in openLCA v2.0.3 using the Ecoinvent v3.9.1 database under a European use-phase context, with a functional unit of 150,000 km. BEVs were evaluated for two representative lithium-ion battery chemistries (NMC622 and LFP) under three electricity carbon intensity scenarios (50, 400, and 850 g CO₂/kWh), while ICEVs were modeled for both gasoline and diesel pathways. Results show that BEV life-cycle GHG emissions vary between 91 and 221 g CO₂-eq/km across different combinations of electricity mix, battery chemistry, and end-of-life conditions. When isolating electricity carbon intensity as the primary variable under a fixed BEV configuration, emissions increase approximately linearly with grid emission factor. Under average European electricity conditions (400 g CO₂/kWh), BEVs exhibit lower life-cycle GHG emissions than gasoline ICEVs, whereas under coal-intensive electricity conditions (850 g CO₂/kWh) this advantage may be reduced or reversed. The break-even electricity carbon intensity is derived by linear interpolation under a fixed BEV configuration (NMC622, 60 kWh, constant lifetime and EoL conditions), yielding a threshold of approximately 600 g CO₂/kWh. The results further indicate that this threshold is influenced by battery chemistry, production-related emissions, recycling efficiency, and assumed vehicle lifetime. These findings highlight the importance of simultaneous progress in electricity decarbonization and end-of-life recycling to secure the environmental benefits of vehicle electrification, and they provide a threshold-oriented framework for policy-relevant interpretation of comparative vehicle LCA results. Full article

The formation of lithium-ion batteries (LIBs) directly affects the properties of the solid electrolyte interphase (SEI) layer, which in turn affects cell performance, lifetime, and safety. Therefore, measurement of SEI properties during formation is a topic of great interest for LIB manufacturing. EIS has previously been applied to half-cell and three-electrode configurations for this purpose; however, these results have been questioned due to the potential non-linearity of the EIS measurement. Additionally, the limited application of the method to half cells and three-electrode cells limits the application of this method to production lines, where only two-electrode full cells are manufactured. In this work, we compare dynamic and steady-state EIS measurements during the formation cycling of NMC532/graphite coin cells. DRT analysis is used to distinguish the time constants of the two electrodes for equivalent circuit modeling. The main findings of this work are that dynamic EIS (DEIS) measurements only significantly affect the frequency response below ~30 Hz. Additionally, time constants and effective capacitance are unaffected by DEIS. We conclude that DEIS remains a promising technique for studying SEI formation in a two-electrode configuration and may be applicable on production lines for rapid diagnostics or even tracking SEI growth in real time. Full article

Both Co-free and lithium- and manganese-rich layered oxide Li(Li_0.2Mn_xNi_yFe_z)O₂ (MNF) cathodes have recently attracted attention in lithium-ion battery (LIB) research due to their high capacities of over 250 mAhg⁻¹, as well as being more eco-friendly and inexpensive than commercial NMC and LiCoO₂. However, they still suffer from lower experimental capacity as well as capacity decay, voltage fade, poor rate capability, and thermal instability. In this paper, fluorine (F)-doped Li_1.2(Mn_0.5Ni_0.2Fe_0.1)O_2(1−x)F_2x (MNF502010, x = 0, 0.025, 0.05, 0.075, 0.1) cathode materials have been synthesized in the nanoscale via sol–gel and subsequent solid-phase calcination to address some of these problems. The resulting 5% F-doped MNF502010 cathode demonstrates the advantage of fluorine doping, which makes a significant contribution to the formation of a well-ordered layer structure with a minimal LiM₂O₄ spinel phase as an impurity. This composition achieves an initial discharge capacity of 252 mAhg⁻¹ (1C = 250 mAhg⁻¹) and a 156 mAhg⁻¹ discharge capacity at 0.3 C on the 100th discharge, with an average voltage fade of 0.24 V. The optimization of fluorine composition results in an enhancement in the activation of the Li₂MnO₃-type monoclinic phase, as well as an increase in the electronic conductivity compared to the fluorine-free cathode. To understand the structural origin of this improved performance, X-ray absorption spectroscopy (XAS) measurements were carried out on pristine and cycled MNF electrodes. Full article

Lithium-ion batteries (LIBs) can considerably improve their lifespan by optimising operating conditions. This may entail ensuring optimal operating temperature, limiting the state-of-charge (SoC) window, reducing cycling current, and changing the physical orientation of the uncompressed LIB cell. In this study, we examine how these four conditions and some of their combinations impact degradation in both 1st life as well as in second life. The cell analysed in this investigation was the Xalt 31 HE cell, an energy-optimised Li-ion pouch cell with a capacity of 31 Ah and an NMC433-graphite chemistry. As a follow-up study of previously reported results, a total of 18 cells were investigated. We report results focusing on improving cycle life and ensuring safety before second life. The optimal conditions for first-life cycling in the full SoC window were found at room temperature, when cycled with a lower current and the cells oriented horizontally. We observed that under the same cycling conditions, a vertical alignment of cells resulted in an increased degradation rate compared to horizontal alignment. The best second-life capacity retention was found for cells initially cycled at room temperature, then later cycled with a reduced SoC window, at a lower current and in a horizontal orientation. If the cells were cycled at an elevated temperature in first life, the second-life compatibility was reduced considerably. An incremental capacity analysis (ICA) of the first-life ageing data revealed a possible indicator for ensuring safety and cycleability into second-life use. Full article

Driven by global urbanization and increasing emphasis on sustainable building practices, indoor volatile organic compounds (VOCs) have emerged as a major environmental and health challenge. This review specifically focuses on room-temperature air-only catalytic oxidation of representative indoor VOCs under a recently matured and highly application-relevant research direction. Recent advances are systematically summarized, highlighting catalyst design strategies, air-phase reaction mechanisms, and performance of noble metal catalysts (NMCs), transition metal oxides (TMOs), bimetallic synergistic catalysts (BSCs), and single-atom catalysts (SACs). Emphasis is placed on thermodynamic feasibility, reaction kinetics, oxidation behavior of non-formaldehyde VOCs, and mechanistic insights associated with SACs interfacial synergy, which enable efficient O₂ activation, high selectivity, and operational stability without external oxidants even under high VOC concentrations. This review provides theoretical foundations and technical guidance for VOCs mitigation and supports the advancement of green, low-carbon, and safe indoor air purification strategies worldwide. Full article

In this design, we propose a completely new sensor structure. It features a metal–insulator–metal (MIM) waveguide and a circular four-stub resonator (CFSR). Using the finite element method, we analyzed the performance of the sensor structure. We examined the impact of different parameters and structural variations on its performance. Ultimately, we determined the optimal performance parameters for the best configuration. The modified device demonstrated a sensitivity (S) of 2940 nm/RIU and a figure of merit (FOM) of 52.5. Furthermore, this sensor design demonstrates significant potential for temperature measurement applications, with a core parameter of 1.508 nm/°C. Full article

The rapid increase in end-of-life lithium-ion batteries demands sustainable recycling routes for lithium recovery. This work presents a novel integrated hydrometallurgical–electrodialysis process designed specifically for recovering lithium from off-specification NMC cathode materials while enabling full reagent recyclability. Selective leaching with oxalic acid was optimised by setting the water-to-oxalic acid dihydrate ratio (H₂O/OA·2H₂O) to 7.3:1 w/w, achieving 81% lithium extraction at room temperature within 2 h while limiting the co-dissolution of Ni, Co and Mn to 0.2%, 1.6% and 1.7% by weight, respectively. The resulting leachate was processed in a four-chamber electrodialysis cell equipped with two Nafion 117 cation-exchange membranes and one Neosepta AMX-fmg anion-exchange membrane operating at −1.6 V versus Ag/AgCl, enabling 96% lithium recovery and 98% oxalic acid recovery. The regenerated oxalic acid stream (41.8 g L⁻¹) was fully restored to its initial concentration and reused in successive cycles without performance loss. Subsequent precipitation of lithium with Na₂CO₃ yielded 99.3%-pure Li₂CO₃. This combined leaching–electrodialysis–precipitation presents a high selectivity, low-waste, circular recovery system, offering a scientifically original approach that integrates reagent regeneration with high-purity lithium production. Full article

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), with different ageing trends. The prognosis algorithms are tuned with three different percentages of the data, allowing for the evaluation of the methods at different ageing stages. The comparison and analysis of the results show that ESN outperforms other methods; it has the lowest prediction error (mean absolute percentage error less than 1.4% at initial ageing of the cells) and the shortest training time, making it the most appropriate method for automotive applications. Full article

Accurate State-of-Health (SOH) estimation for second-life batteries is a critical challenge due to unavailable first-life historical data. This work introduces the Prior-Informed Hierarchical Fusion Network (PI-HFN), a flexible deep learning framework to address this information asymmetry. The model leverages a historical degradation vector from the battery’s first life as a prior condition to guide the interpretation of its current electrochemical state. This prior-conditioning is achieved by initializing a recurrent neural network, which processes instantaneous second-life data, with the historical information. The framework’s efficacy was validated across two comprehensive case studies. In Case Study 1 on NMC cells using multimodal EIS and DCIR inputs, the PI-HFN achieved a state-of-the-art average RMSE of 1.91%. Case Study 2 on a large-scale LFP dataset, using only DCIR inputs, further demonstrated the framework’s robustness and flexibility. Both studies confirmed through systematic ablation studies that the historical prior (BFN) and the hierarchical fusion strategy (PFN) are crucial for high accuracy, with the historical prior proving to be the dominant factor in mitigating errors in data-sparse SOH regions. The model also shows excellent deployment potential, with an inference latency of 15.8 ms on an RK3568 edge platform after INT8 quantization. This study validates that conditioning deep learning models with historical priors offers a robust, flexible, and practical pathway for evaluating second-life batteries, supporting their integration into the circular economy. Full article

India’s rapid growth in electric vehicles and renewable energy systems is driving strong growth in lithium-ion battery demand. This study provides an India-specific life cycle assessment of manufacturing using imported primary materials with pathways incorporating domestically recycled materials. Two battery chemistries of strategic relevance to India, nickel-manganese-cobalt (NMC 532) and lithium iron phosphate (LFP), were evaluated using a functional unit of 1 kWh battery pack. The ReCiPe midpoint method was used to quantify the environmental impacts, with a focus on major emission indicators (CO₂, NO_x, SO_x, and PM₁₀) in the Indian electricity mix. The results show that NMC 532 batteries exhibit higher emissions than LFP batteries, largely due to the energy-intensive production of nickel and cobalt sulphate precursors. The incorporation of recycled materials substantially reduces emissions for both chemistries. It decreases by 30% for NMC532 and 36% for LFP. Hotspot analysis shows that precursor production, electricity use, and chemical inputs in hydrometallurgical recycling are the main causes of the remaining effects. This study shows that integrating recycling to India’s LIB supply chain improves climate and air quality outcomes, enhances critical mineral recovery and supports sustainable manufacturing through circular economy pathways for India’s battery and clean energy transition. Full article