40 Years of Low-Temperature Electrolytes for Rechargeable Lithium Batteries.

Rechargeable lithium batteries are one of the most appropriate energy storage systems in our electrified society, as virtually all portable electronic devices and electric vehicles today rely on the chemical energy stored in them. However, sub-zero Celsius operation, especially below -20 °C, remains a huge challenge for lithium batteries and greatly limits their application in extreme environments. Slow Li+ diffusion and charge transfer kinetics have been identified as two main origins of the poor performance of RLBs under low-temperature conditions, both strongly associated with the liquid electrolyte that governs bulk and interfacial ion transport. In this review, we first analyze the low-temperature kinetic behavior and failure mechanism of lithium batteries from an electrolyte standpoint. We next trace the history of low-temperature electrolytes in the past 40 years (1983-2022), followed by a comprehensive summary of the research progress as well as introducing the state-of-the-art characterization and computational methods for revealing their underlying mechanisms. Finally, we provide some perspectives on future research of low-temperature electrolytes with particular emphasis on mechanism analysis and practical application.

Taming Solvent-Solute Interaction Accelerates Interfacial Kinetics in Low-Temperature Lithium-Metal Batteries.

Lithium (Li)-metal batteries promise energy density beyond 400 Wh kg-1 , while their practical operation at an extreme temperature below -30 °C suffers severe capacity deterioration. Such battery failure highly relates to the remarkably increased kinetic barrier of interfacial processes, including interfacial desolvation, ion transportation, and charge transfer. In this work, the interfacial kinetics in three prototypical electrolytes are quantitatively probed by three-electrode electrochemical techniques and molecular dynamics simulations. Desolvation as the limiting step of interfacial processes is validated to dominate the cell impedance and capacity at low temperature. 1,3-Dioxolane-based electrolyte with tamed solvent-solute interaction facilitates fast desolvation, enabling the practical Li|LiNi0.5 Co0.2 Mn0.3 O2 cells at -40 °C to retain 66% of room-temperature capacity and withstand remarkably fast charging rate (0.3 C). The barrier of desolvation dictated by solvent-solute interaction environments is quantitatively uncovered. Regulating the solvent-solute interaction by low-affinity solvents emerges as a promising solution to low-temperature batteries.

The Crucial Role of Electrode Potential of a Working Anode in Dictating the Structural Evolution of Solid Electrolyte Interphase.

The performance of rechargeable lithium (Li) batteries is highly correlated with the structure of solid electrolyte interphase (SEI). The properties of a working anode are vital factors in determining the structure of SEI; however, the correspondingly poor understanding hinders the rational regulation of SEI. Herein, the electrode potential and anode material, two critical properties of an anode, in dictating the structural evolution of SEI were investigated theoretically and experimentally. The anode potential is identified as a crucial role in dictating the SEI structure. The anode potential determines the reduction products in the electrolyte, ultimately giving rise to the mosaic and bilayer SEI structure at high and low potential, respectively. In contrast, the anode material does not cause a significant change in the SEI structure. This work discloses the crucial role of electrode potential in dictating SEI structure and provides rational guidance to regulate SEI structure.

Multi-Conditional Constraint Generative Adversarial Network-Based MR Imaging from CT Scan Data.

Magnetic resonance (MR) imaging is an important computer-aided diagnosis technique with rich pathological information. The factor of physical and physiological constraint seriously affects the applicability of that technique. Thus, computed tomography (CT)-based radiotherapy is more popular on account of its imaging rapidity and environmental simplicity. Therefore, it is of great theoretical and practical significance to design a method that can construct an MR image from the corresponding CT image. In this paper, we treat MR imaging as a machine vision problem and propose a multi-conditional constraint generative adversarial network (GAN) for MR imaging from CT scan data. Considering reversibility of GAN, both generator and reverse generator are designed for MR and CT imaging, respectively, which can constrain each other and improve consistency between features of CT and MR images. In addition, we innovatively treat the real and generated MR image discrimination as object re-identification; cosine error fusing with original GAN loss is designed to enhance verisimilitude and textural features of the MR image. The experimental results with the challenging public CT-MR image dataset show distinct performance improvement over other GANs utilized in medical imaging and demonstrate the effect of our method for medical image modal transformation.

Modification of Nitrate Ion Enables Stable Solid Electrolyte Interphase in Lithium Metal Batteries.

The lifespan of high-energy-density lithium metal batteries (LMBs) is hindered by heterogeneous solid electrolyte interphase (SEI). The rational design of electrolytes is strongly considered to obtain uniform SEI in working batteries. Herein, a modification of nitrate ion (NO3 - ) is proposed and validated to improve the homogeneity of the SEI in practical LMBs. NO3 - is connected to an ether-based moiety to form isosorbide dinitrate (ISDN) to break the resonance structure of NO3 - and improve the reducibility. The decomposition of non-resonant -NO3 in ISDN enriches SEI with abundant LiNx Oy and induces uniform lithium deposition. Lithium-sulfur batteries with ISDN additives deliver a capacity retention of 83.7 % for 100 cycles compared with rapid decay with LiNO3 after 55 cycles. Moreover, lithium-sulfur pouch cells with ISDN additives provide a specific energy of 319 Wh kg-1 and undergo 20 cycles. This work provides a realistic reference in designing additives to modify the SEI for stabilizing LMBs.

A Successive Conversion-Deintercalation Delithiation Mechanism for Practical Composite Lithium Anodes.

Lithium (Li) metal anodes are attractive for high-energy-density batteries. Dead Li is inevitably generated during the delithiation of deposited Li based on a conversion reaction, which severely depletes active Li and electrolyte and induces a short lifespan. In this contribution, a successive conversion-deintercalation (CTD) delithiation mechanism is proposed by manipulating the overpotential of the anode to restrain the generation of dead Li. The delithiation at initial cycles is solely carried out by a conversion reaction of Li metal. When the overpotential of the anode increases over the delithiation potential of lithiated graphite after cycling, a deintercalation reaction is consequently triggered to complete a whole CTD delithiation process, largely reducing the formation of dead Li due to a highly reversible deintercalation reaction. Under practical conditions, the working batteries based on a CTD delithiation mechanism maintain 210 cycles with a capacity retention of 80% in comparison to 110 cycles of a bare Li anode. Moreover, a 1 Ah pouch cell with a CTD delithiation mechanism operates for 150 cycles. The work ingeniously restrains the generation of dead Li by manipulating the delithiation mechanisms of the anode and contributes to a fresh concept for the design of practical composite Li anodes.

Reclaiming Inactive Lithium with a Triiodide/Iodide Redox Couple for Practical Lithium Metal Batteries.

High-energy-density lithium (Li) metal batteries suffer from a short lifespan owing to apparently ceaseless inactive Li accumulation, which is accompanied by the consumption of electrolyte and active Li reservoir, seriously deteriorating the cyclability of batteries. Herein, a triiodide/iodide (I3 - /I- ) redox couple initiated by stannic iodide (SnI4 ) is demonstrated to reclaim inactive Li. The reduction of I3 - converts inactive Li into soluble LiI, which then diffuses to the cathode side. The oxidation of LiI by the delithiated cathode transforms cathode into the lithiation state and regenerates I3 - , reclaiming Li ion from inactive Li. The regenerated I3 - engages the further redox reactions. Furthermore, the formation of Sn mitigates the corrosion of I3 - on active Li reservoir sacrificially. In working Li | LiNi0.5 Co0.2 Mn0.3 O2 batteries, the accumulated inactive Li is significantly reclaimed by the reversible I3 - /I- redox couple, improving the lifespan of batteries by twice. This work initiates a creative solution to reclaim inactive Li for prolonging the lifespan of practical Li metal batteries.

Spine Computed Tomography to Magnetic Resonance Image Synthesis Using Generative Adversarial Networks : A Preliminary Study.

OBJECTIVE: To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. METHODS: GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). RESULTS: The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). CONCLUSION: This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.

Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors for the Advanced Driver-Assistance System (ADAS).

This paper proposes a self-calibration method that can be applied for multiple larger field-of-view (FOV) camera models on an advanced driver-assistance system (ADAS). Firstly, the proposed method performs a series of pre-processing steps such as edge detection, length thresholding, and edge grouping for the segregation of robust line candidates from the pool of initial distortion line segments. A novel straightness cost constraint with a cross-entropy loss was imposed on the selected line candidates, thereby exploiting that novel loss to optimize the lens-distortion parameters using the Levenberg-Marquardt (LM) optimization approach. The best-fit distortion parameters are used for the undistortion of an image frame, thereby employing various high-end vision-based tasks on the distortion-rectified frame. In this study, an investigation was carried out on experimental approaches such as parameter sharing between multiple camera systems and model-specific empirical γ -residual rectification factor. The quantitative comparisons were carried out between the proposed method and traditional OpenCV method as well as contemporary state-of-the-art self-calibration techniques on KITTI dataset with synthetically generated distortion ranges. Famous image consistency metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and position error in salient points estimation were employed for the performance evaluations. Finally, for a better performance validation of the proposed system on a real-time ADAS platform, a pragmatic approach of qualitative analysis has been conducted through streamlining high-end vision-based tasks such as object detection, localization, and mapping, and auto-parking on undistorted frames.

Deep CT to MR Synthesis Using Paired and Unpaired Data.

Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This study is aimed towards patients who are contraindicated owing to claustrophobia and cardiac pacemakers, and many scenarios in which only computed tomography (CT) images are available, such as emergencies, situations lacking an MR scanner, and situations in which the cost of obtaining an MR scan is prohibitive. From medical practice, our approach can be adopted as a screening method by radiologists to observe abnormal anatomical lesions in certain diseases that are difficult to diagnose by CT. The proposed approach can estimate an MR image based on a CT image using paired and unpaired training data. In contrast to existing synthetic methods for medical imaging, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration of paired training, and overcomes the context-misalignment problem of unpaired training. A generative adversarial network was trained to transform two-dimensional (2D) brain CT image slices into 2D brain MR image slices, combining the adversarial, dual cycle-consistent, and voxel-wise losses. Qualitative and quantitative comparisons against independent paired and unpaired training methods demonstrated the superiority of our approach.