Quantitative Electroencephalography and Balance Test Correlations in Patients with Chronic Patellofemoral Pain.

Background: Quantitative Electroencephalography (QEEG) is a tool helping better understand the electrical activity of the brain and a non-invasive method to assess cortical activity. To date, the brain activity of patients with chronic patellofemoral pain (PFP) has not been investigated. Objective: The current study aimed to investigate the effect of PFP on higher levels of the central nervous system by assessing the correlation between QEEG and modified excursion balance test (mSEBT) in patients with PFP. Material and Methods: Twenty-two patients with chronic PFP participated in this observational study. Their cortical electrical activity was recorded in a resting state with their eyes open, via a 32-channel QEEG. C3, C4, and Cz were considered as regions of interest. In addition to QEEG, the balance performance of the participants was evaluated via mSEBT. Results: The obtained findings revealed a negative and moderate to high correlation between theta absolute power and posteromedial direction of mSEBT in C4 (P 0.000, r -0.68), Cz (P 0.001, r -0.66), and C3 (P 0.000, r -0.70). Additionally, a significantly close correlation is between alpha absolute power in C3 (P 0.001, r -0.70), C4 (P 0.000, r -0.71), and Cz (P 0.000, r -0.74) and the posteromedial direction of mSEBT. No significant correlations were between the other two directions of mSEBT, alpha, and theta. Conclusion: According to our results, balance impairment in patients with chronic PFP correlated with their QEEG neurodynamics. Moreover, our findings demonstrated the efficiency of QEEG as a neuromodulation method for patients with PFP.

Discovering 3D hidden elasticity in isotropic and transversely isotropic materials with physics-informed UNets.

Three-dimensional variation in structural components or fiber alignments results in complex mechanical property distribution in tissues and biomaterials. In this paper, we use a physics-informed UNet-based neural network model (El-UNet) to discover the three-dimensional (3D) internal composition and space-dependent material properties of heterogeneous isotropic and transversely isotropic materials without a priori knowledge of the composition. We then show the capabilities of El-UNet by validating against data obtained from finite-element simulations of two soft tissues, namely, brain tissue and articular cartilage, under various loading conditions. We first simulated compressive loading of 3D brain tissue comprising of distinct white matter and gray matter mechanical properties undergoing small strains with isotropic linear elastic behavior, where El-UNet reached mean absolute relative errors under 1.5 % for elastic modulus and Poisson's ratio estimations across the 3D volume. We showed that the 3D solution achieved by El-UNet was superior to relative stiffness mapping by inverse of axial strain and two-dimensional plane stress/plane strain approximations. Additionally, we simulated a transversely isotropic articular cartilage with known fiber orientations undergoing compressive loading, and accurately estimated the spatial distribution of all five material parameters, with mean absolute relative errors under 5 %. Our work demonstrates the application of the computationally efficient physics-informed El-UNet in 3D elasticity imaging and provides methods for translation to experimental 3D characterization of soft tissues and other materials. The proposed El-UNet offers a powerful tool for both in vitro and ex vivo tissue analysis, with potential extensions to in vivo diagnostics. STATEMENT OF SIGNIFICANCE: Elasticity imaging is a technique that reconstructs mechanical properties of tissue using deformation and force measurements. Given the complexity of this reconstruction, most existing methods have mostly focused on 2D problems. Our work is the first implementation of physics-informed UNets to reconstruct three-dimensional material parameter distributions for isotropic and transversely isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using synthetic data generated using finite element models of biological tissues with high bio-fidelity-the brain and articular cartilage. Our method can be implemented in elasticity imaging scenarios for in vitro and ex vivo mechanical characterization of biomaterials and biological tissues, with potential extensions to in vivo diagnostics.

rGO/ZIF-8 Aerogel for Effective Removal of Malachite Green from Wastewater.

In this study, dopamine-modified graphene aerogel (DGA) is synthesized through a one-step hydrothermal method using graphene oxide as the precursor and dopamine as the reducing agent. Subsequently, in situ immersion synthesis is conducted to obtain ZIF-8 loaded on a dopamine-modified graphene aerogel skeleton (ZDGA), featuring a regular honeycomb interconnected mesoporosity and a high specific surface area of 532.8 m2/g. The synthesized ZDGA exhibited exceptional adsorption performance for the cationic dye malachite green. At room temperature, ZDGA achieved an impressive equilibrium adsorption capacity of 6578.34 mg/g. The adsorption process followed pseudo-secondary kinetics and adhered to the Langmuir model, indicating chemically dominated adsorption on a monomolecular layer. Intraparticle diffusion was the primary rate determinant, with π-π stacking, electrostatic adsorption, hydrogen bonding, and Lewis acid-base interactions serving as the key driving forces. It has an ideal specific surface area and good cycling performance, which highlights its potential application in dye wastewater treatment.

Generation mechanism and empirical model of eddy current force and torque in drum-type eddy current separation.

Enhancing the recovery efficiency of non-ferrous metals in eddy current separation is of great significance. In this study, the accuracy of the simulation model was verified by comparing the eddy current force. The transformation mechanism of the Lorentz forces into the eddy current force and torque in non-ferrous metal particles was revealed by analyzing various physical fields. Then, the influence of magnetic field parameters on eddy current, eddy current force, and torque was studied. It shows that the eddy current force and torque are affected by the vector gradient of the magnetic field and the magnetic flux density, respectively. Additionally, the time derivative of the magnetic field impacts the magnitude of the eddy current force and torque by controlling the eddy current. On this basis, the empirical models of eddy current force and torque were established by similarity theory. The results obtained can improve and expand the application of eddy current separation.

Stoichiometrically optimized eg orbital occupancy of Ni-Co oxide catalysts for Li-air batteries.

Li-air battery (LAB) technology is making continuous progress toward its theoretical capacity, which is comparable to gasoline. However, the sluggish reaction at the cathode is still a challenge. We propose a simple strategy to optimize the surface eg occupancy by adjusting the stoichiometric ratios of transition metal-based spinel structures through a controlled hydrothermal synthesis. Three distinct stoichiometries of Ni-Co oxides were used to demonstrate the direct correlation between stoichiometry and catalytic performance. The groundsel flower-like structure having a 1 : 1.4 Ni : Co atomic ratio with high surface area, high defect density, and an abundance of Ni3+ at the surface with semi-filled eg orbitals was found to benefit the structure promoting high catalytic activities in aqueous and aprotic media. The assembled LAB cells employing this cathode demonstrate an exceptional lifespan, operating for 3460 hours and completing 173 cycles while achieving the highest discharge capacity of 13 759 mA h g-1 and low charging overpotentials. The key to this prolonged performance lies in the full reversibility of the cell, attributed to its excellent OER performance. A well-surface adsorbed, amorphous LiO2/Li2O2 discharge product is found to possess high diffusivity and ease of decomposition, contributing significantly to the enhanced longevity of the cell.

XGB Modeling Reveals Improvement of Compressive Strength of Cement-Based Composites with Addition of HPMC and Chitosan.

This study investigates the improvement in the compressive strength of cellulose/cement-based composites. Methyl cellulose (MC), carboxymethyl cellulose (CMC), and hydroxypropyl cellulose (HPMC) are separately used as the cellulose phase with different wt%. Graphene oxide (GO) and zoledronic acid (ZOL) are used as additives for bone regeneration for various formulations. Utilizing Extreme Gradient Boosting (XGB) modeling, this research demonstrates the roles of the choice of the cellulose phase, wt% of cement phase, % gelatin, % citric acid, degradation time, and concentration of GO and ZOL in influencing compressive strength. The XGB regression model, with an R2 value of 0.99 (~1), shows the predictive power of the model. Feature importance analysis demonstrates the significance of cellulose choice and the addition of chitosan in enhancing compressive strength. The correlation heatmap reveals positive associations, emphasizing the positive influence of HPMC and CMC compared with MC and the substantial impact of chitosan and citric acid on compressive strength. The model's predictive accuracy is validated through predicted compressive strength values with experimental observations, providing insights for optimizing cellulose-reinforced cements and enabling tailored material design for enhanced mechanical performance.

Transforming Nature's Bath Sponge into Stacking Faults-Enhanced Ag Nanorings-Decorated Catalyst for Hydrogen Evolution Reaction.

The rational design of cost-effective and efficient electrocatalysts for electrochemical water splitting is essential for green hydrogen production. Utilizing nanocatalysts with abundant active sites, high surface area, and deliberate stacking faults is a promising approach for enhancing catalytic efficiency. In this study, we report a simple strategy to synthesize a highly efficient electrocatalyst for the hydrogen evolution reaction (HER) using carbonized luffa cylindrica as a conductive N-doped carbon skeleton decorated with Ag nanorings that are activated by introducing stacking faults. The introduction of stacking faults and the resulting tensile strain into the Ag nanorings results in a significant decrease in the HER overpotential, enabling the use of Ag as an efficient HER electrocatalyst. Our findings demonstrate that manipulating the crystal properties of electrocatalysts, even for materials with intrinsically poor catalytic activity such as Ag, can result in highly efficient catalysts. Further, applying a conductive carbon backbone can lower the quantities of metal needed without compromising the HER activity. This approach opens up new avenues for designing high-performance electrocatalysts with very low metallic content, which could significantly impact the development of sustainable and cost-effective electrochemical water-splitting systems.

Comparison of cytotoxicity of Miltefosine and its niosomal form on chick embryo model.

Various drugs have been used for the treatment of leishmaniasis, but they often have adverse effects on the body's organs. In this study, we aimed to explore the effects of one type of drug, Miltefosine (MIL), and its analogue or modifier, liposomal Miltefosine (NMIL), on several fetal organs using both in silico analysis and practical tests on chicken embryos. Our in silico approach involved predicting the affinities of MIL and NMIL to critical proteins involved in leishmaniasis, including Vascular Endothelial Growth Factor A (VEGF-A), the Kinase insert domain receptor (KDR1), and apoptotic-regulator proteins (Bcl-2-associate). We then validated and supported these predictions through in vivo investigations, analyzing gene expression and pathological changes in angiogenesis and apoptotic mediators in MIL- and NMIL-treated chicken embryos. The results showed that NMIL had a more effective action towards VEGF-A and KDR1 in leishmaniasis, making it a better candidate for potential operative treatment during pregnancy than MIL alone. In vivo, studies also showed that chicken embryos under MIL treatment displayed less vascular mass and more degenerative and apoptotic changes than those treated with NMIL. These results suggest that NMIL could be a better treatment option for leishmaniasis during pregnancy.

Physics-informed UNets for discovering hidden elasticity in heterogeneous materials.

Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance - both in terms of accuracy and computational cost - by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element simulations of isotropic domains with heterogenous distributions of material parameters to generate synthetic data. El-UNet is faster and more accurate than the fully-connected physics-informed implementation in resolving the distribution of unknown fields. Among the tested models, the self-adaptive spatially weighted models had the most accurate reconstructions in equal computation times. The learned spatial weighting distribution visibly corresponded to regions that the unweighted models were resolving inaccurately. Our work demonstrates a computationally efficient inversion algorithm for elasticity imaging using convolutional neural networks and presents a potential fast framework for three-dimensional inverse elasticity problems that have proven unachievable through previously proposed methods.

A combined gas-phase dissociative ionization, dissociative electron attachment and deposition study on the potential FEBID precursor [Au(CH3)2Cl]2.

Motivated by the potential of focused-electron-beam-induced deposition (FEBID) in the fabrication of functional gold nanostructures for application in plasmonic and detector technology, we conducted a comprehensive study on [Au(CH3)2Cl]2 as a potential precursor for such depositions. Fundamental electron-induced dissociation processes were studied under single collision conditions, and the composition and morphology of FEBID deposits fabricated in an ultrahigh-vacuum (UHV) chamber were explored on different surfaces and at varied beam currents. In the gas phase, dissociative ionization was found to lead to significant carbon loss from this precursor, and about 50% of the chlorine was on average removed per dissociative ionization incident. On the other hand, in dissociative electron attachment, no chlorine was removed from the parent molecule. Contrary to these observations, FEBID in the UHV setup was found to yield a quantitative loss and desorption of the chlorine from the deposits, an effect that we attribute to electron-induced secondary and tertiary reactions in the deposition process. We find this precursor to be stable at ambient conditions and to have sufficient vapor pressure to be suitable for use in HV instruments. More importantly, in the UHV setup, FEBID with [Au(CH3)2Cl]2 yielded deposits with high gold content, ranging from 45 to 61 atom % depending on the beam current and on the cleanliness of the substrates surface.

Immune checkpoints and cancer immunotherapies: insights into newly potential receptors and ligands.

Checkpoint markers and immune checkpoint inhibitors have been increasingly identified and developed as potential immunotherapeutic targets in various human cancers. Despite valuable efforts to discover novel immune checkpoints and their ligands, the precise roles of their therapeutic functions, as well as the broad identification of their counterpart receptors, remain to be addressed. In this context, it has been suggested that various putative checkpoint receptors can be induced upon activation. In the tumor microenvironment, T cells, as crucial immune response against malignant diseases as well as other immune central effector cells, such as natural killer cells, are regulated via co-stimulatory or co-inhibitory signals from immune or tumor cells. Studies have shown that exposure of T cells to tumor antigens upregulates the expression of inhibitory checkpoint receptors, leading to T-cell dysfunction or exhaustion. Although targeting immune checkpoint regulators has shown relative clinical efficacy in some tumor types, most trials in the field of cancer immunotherapies have revealed unsatisfactory results due to de novo or adaptive resistance in cancer patients. To overcome these obstacles, combinational therapies with newly discovered inhibitory molecules or combined blockage of several checkpoints provide a rationale for further research. Moreover, precise identification of their receptors counterparts at crucial checkpoints is likely to promise effective therapies. In this review, we examine the prospects for the application of newly emerging checkpoints, such as T-cell immunoglobulin and mucin domain 3, lymphocyte activation gene-3, T-cell immunoreceptor with Ig and ITIM domains (TIGIT), V-domain Ig suppressor of T-cell activation (VISTA), new B7 family proteins, and B- and T-cell lymphocyte attenuator, in association with immunotherapy of malignancies. In addition, their clinical and biological significance is discussed, including their expression in various human cancers, along with their roles in T-cell-mediated immune responses.

Green conversion of waste PET into magnetic Ni0·4Fe2·6O4/(Fe,Ni)@carbon nanostructure for adsorption and separation of dyes from aqueous media.

A nanostructured core-shell composite (Ni0·4Fe2·6O4/(Fe,Ni)@carbon, NFC) comprising magnetic nano-cores encapsulated with graphitic shells (≈80 wt%) is prepared by facile and clean mechanochemical-molten salt processing approach using waste PET; providing a specific surface area of 201.9 m2 g-1, well-developed mesopores, and ferromagnetic behavior characterized by the coercivity value of 149 Oe. NFC is utilized as a high-performance adsorbent for the removal of organic dyes from their aqueous solutions. Moreover, the magnetic performance of NFC enables the facile collection of the exhausted adsorbent out of the purified water. Performances of NFC for the removal of crystal violet dye (CV), methyl orange (MO) and rhodamine B (Rh B) from their aqueous solutions are systematically investigated under different environmental conditions including the adsorbent dosage and dye concentration, as well as the solution pH and temperature, where an impressive CV removal capacity of 201.6-243.8 mg g-1 is recorded for a wide pH range of 2-10. Mechanism and kinetics involved in the adsorption process are investigated by studying the adsorption isotherms and thermodynamics. The dye adsorption of the nanocomposite material is confirmed to follow the pseudo-second-order kinetic model combined with the Langmuir isotherm model, exhibiting an excellent spontaneous and exothermic monolayer adsorption capacity of around 153 mg g-1 (for MO) for the fresh adsorbent and around 89 mg g-1 after three adsorption-regeneration cycles.

Carbonization of Corn Leaf Waste for Na-Ion Storage Application Using Water-Soluble Carboxymethyl Cellulose Binder.

Hard carbon materials are considered to be the most practical anode materials for sodium ion batteries because of the rich availability of their resources and potentially low cost. Here, the conversion of corn leaf biomass, a largely available agricultural waste, into carbonaceous materials for Na-ion storage application is reported. Thermal analysis investigation determines the presence of exothermic events occurring during the thermal treatment of the biomass. Accordingly, various temperatures of 400, 500, and 600 °C are selected to perform carbonization treatment trials, leading to the formation of various biocarbons. The materials obtained are characterized by a combination of methods, including X-ray diffraction, electron microscopy, surface evaluation, Raman spectroscopy, and electrochemical characterizations. The Na-ion storage performances of these materials are investigated using water-soluble carboxymethyl cellulose binder, highlighting the influence of the carbonization temperature on the electrochemical performance of biocarbons. Moreover, the influence of post-mechanochemical treatment on the Na-ion storage performance of biocarbons is studied through kinetic evaluations. It is confirmed that reducing the particle sizes and increasing the carbon purity of biocarbons and the formation of gel polymeric networks would improve the Na-ion storage capacity, as well as the pseudocapacitive contribution to the total current. At a high-current density of 500 mA g-1, a specific Na-ion storage capacity of 134 mAh g-1 is recorded on the biocarbon prepared at 600 °C, followed by ball-milling and washing treatment, exhibiting a reduced charge transfer resistance of 49 Ω and an improved Na-ion diffusion coefficient of 4.8 × 10-19 cm2 s-1. This article proposes a simple and effective technique for the preparation of low-cost biocarbons to be used as the anode of Na-ion batteries.

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer's disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer's disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.

The potential impact of emotionally loaded stimuli on over/under-estimating neutral situations among power plant control-room operators.

BACKGROUND: Human emotions vary on a contextual basis. The significance of emotions becomes even more salient in Control Room Operators (CROs) in power plants. OBJECTIVE: This study investigated the effects of emotionally loaded pictures on over/underestimating neutral situations. METHODS: Twenty CROs voluntarily enrolled in the present study. Twenty-one blocks were considered, including ten emotionally loaded and 11 neutral blocks. The stimuli were alternatively submitted to subjects in random order. Each block comprised 13 images from the International Affective Picture System (IAPS), which were shown for 5 seconds. Subjects were required to complete the Self-Assessment Manikin (SAM) after exposure to the first and the last neutral blocks, which were identical. RESULTS: Our analyses showed significant differences between IAPS arousal and SAM1 and SAM2 arousal ratings (p SAM1&IAPS = 0.00, p SAM2&IAPS = 0.02). There was no significant relationship between the first and the second arousals, and emotionally loaded images corresponded to no significant difference in terms of valence. CONCLUSION: The findings suggested that the participants overestimated neutral situations compared with IAPS only in arousal level. Furthermore, CROs can still retain their ability to assess neutral situations in the case of viewing emotional stimuli, especially in valence level, at least half an hour after the first rating. A study design with pure negative/positive and high arousal levels may still provide even more significant results.

Sleep Micro-Macro-structures in Psychophysiological Insomnia. PSG Study.

Study Objectives: To address sleep micro-macro-structures in psychophysiological insomnia (PPI) as denoted by cyclic alternating pattern (CAP), Sleep spindles, and hyperarousal as microstructures and sleep characteristics such as sleep stages' variables, and heart rate as macrostructures. Methods: Two statistical populations, with 20 participants in each, are addressed: good sleepers (GS) and patients with psychophysiological insomnia (PPI). The sleep polysomnography (PSG) for one night was performed and sleep macro-micro-structures extraction was implemented for each participant. Cyclic alternating patterns were scored manually and other structures were monitored by the original PSG's device software. Analytical methods are used to dissect the results. Result: The findings imply: (a) psychophysiological insomnia is characterized by CAP differences from good sleepers which are associated with hyperarousal; (b) Regarding microstructure, more microarousals in sleep stages caused more number of wake index. (c) The ratio of sleep stages, sleep latency and heart rate as sleep macrostructure are significantly changed. (d) There is no significant difference between PPI and GS groups on spindles length in our research. Conclusion: Regarding all sleep disorders and especially PPI, CAP variables, EEG arousals, and sleep spindles as microstructures and Total Sleep Time, Sleep Latency, number of waking, REM duration, and Heart Rate as macrostructures were found to be critical for the diagnosis of psychophysiological insomnia The analysis contributes to understanding better approaches in the quantitative specification of psychophysiological insomnia compare to good sleepers.

Ultrasound, photoacoustic, and magnetic resonance imaging to study hyperacute pathophysiology of traumatic and vascular brain injury.

BACKGROUND AND PURPOSE: Cerebrovascular dynamics and pathomechanisms that evolve in the minutes and hours following traumatic vascular injury in the brain remain largely unknown. We investigated the pathophysiology evolution in mice within the first 3 hours after closed-head traumatic brain injury (TBI) and subarachnoid hemorrhage (SAH), two significant traumatic vascular injuries. METHODS: We took a multimodal imaging approach using photoacoustic imaging, color Doppler ultrasound, and MRI to track injury outcomes using a variety of metrics. RESULTS: Brain oxygenation and velocity-weighted volume of blood flow (VVF) values significantly decreased from baseline to 15 minutes after both TBI and SAH. TBI resulted in 19.2% and 41.0% ipsilateral oxygenation and VVF reductions 15 minutes postinjury, while SAH resulted in 43.9% and 85.0% ipsilateral oxygenation and VVF reduction (p < .001). We found partial recovery of oxygenation from 15 minutes to 3 hours after injury for TBI but not SAH. Hemorrhage, edema, reduced perfusion, and altered diffusivity were evident from MRI scans acquired 90-150 minutes after injury in both injury models, although the spatial distribution was mostly focal for TBI and diffuse for SAH. CONCLUSIONS: The results reveal that the cerebral oxygenation deficits immediately following injuries are reversible for TBI and irreversible for SAH. Our findings can inform future studies on mitigating these early responses to improve long-term recovery.

Surface Modification of Transition Metal Dichalcogenide Nanosheets for Intrinsically Self-Healing Hydrogels with Enhanced Mechanical Properties.

Nanocomposites with enhanced mechanical properties and efficient self-healing characteristics can change how the artificially engineered materials' life cycle is perceived. Improved adhesion of nanomaterials with the host matrix can drastically improve the structural properties and confer the material with repeatable bonding/debonding capabilities. In this work, exfoliated 2H-WS2 nanosheets are modified using an organic thiol to impart hydrogen bonding sites on the otherwise inert nanosheets by surface functionalization. These modified nanosheets are incorporated within the PVA hydrogel matrix and analyzed for their contribution to the composite's intrinsic self-healing and mechanical strength. The resulting hydrogel forms a highly flexible macrostructure with an impressive enhancement in mechanical properties and a very high autonomous healing efficiency of 89.92%. Interesting changes in the surface properties after functionalization show that such modification is highly suitable for water-based polymeric systems. Probing into the healing mechanism using advanced spectroscopic techniques reveals the formation of a stable cyclic structure on the surface of nanosheets, mainly responsible for the improved healing response. This work opens an avenue toward the development of self-healing nanocomposites where chemically inert nanoparticles participate in the healing network rather than just mechanically reinforcing the matrix by slender adhesion.

Sustainable regeneration of high-performance LiCoO2 from completely failed lithium-ion batteries.

Utilising the solid-state synthesis method is an easy and effective way to recycle spent lithium-ion batteries. However, verifying its direct repair effects on completely exhausting cathode materials is necessary. In this work, the optimal conditions for direct repair of completely failed cathode materials by solid-state synthesis are explored. The discharge capacity of spent LiCoO2 cathode material is recovered from 21.7 mAh g-1 to 138.9 mAh g-1 under the optimal regeneration conditions of 850 °C and n(Li)/n(Co) ratio of 1:1. The regenerated materials show excellent electrochemical performance, even greater than the commercial LiCoO2. In addition, based on the whole closed-loop recycling process, the economic and environmental effects of various recycling techniques and raw materials used in the battery production process are assessed, confirming the superior economic and environmental feasibility of direct regeneration method.