Elastomeric electrolytes for high-energy solid-state lithium batteries.

The use of lithium metal anodes in solid-state batteries has emerged as one of the most promising technologies for replacing conventional lithium-ion batteries1,2. Solid-state electrolytes are a key enabling technology for the safe operation of lithium metal batteries as they suppress the uncontrolled growth of lithium dendrites. However, the mechanical properties and electrochemical performance of current solid-state electrolytes do not meet the requirements for practical applications of lithium metal batteries. Here we report a class of elastomeric solid-state electrolytes with a three-dimensional interconnected plastic crystal phase. The elastomeric electrolytes show a combination of mechanical robustness, high ionic conductivity, low interfacial resistance and high lithium-ion transference number. The in situ-formed elastomer electrolyte on copper foils accommodates volume changes for prolonged lithium plating and stripping processes with a Coulombic efficiency of 100.0 per cent. Moreover, the elastomer electrolytes enable stable operation of the full cells under constrained conditions of a limited lithium source, a thin electrolyte and a high-loading LiNi0.83Mn0.06Co0.11O2 cathode at a high voltage of 4.5 volts at ambient temperature, delivering a high specific energy exceeding 410 watt-hours per kilogram of electrode plus electrolyte. The elastomeric electrolyte system presents a powerful strategy for enabling stable operation of high-energy, solid-state lithium batteries.

Structure-Controlled Carbon Hosts for Dendrite-Free Aqueous Zinc Batteries.

The surging demand for environmental-friendly and safe electrochemical energy storage systems has driven the development of aqueous zinc (Zn)-ion batteries (ZIBs). However, metallic Zn anodes suffer from severe dendrite growth and large volume change, resulting in a limited lifetime for aqueous ZIB applications. Here, it is shown that 3D mesoporous carbon (MC) with controlled carbon and defect configurations can function as a highly reversible and dendrite-free Zn host, enabling the stable operation of aqueous ZIBs. The MC host has a structure-controlled architecture that contains optimal sp2 -carbon and defect sites, which results in an improved initial nucleation energy barrier and promotes uniform Zn deposition. As a consequence, the MC host shows outstanding Zn plating/stripping performance over 1000 cycles at 2 mA cm-2 and over 250 cycles at 6 mA cm-2 in asymmetric cells. Density functional theory calculations further reveal the role of the defective sp2 -carbon surface in Zn adsorption energy. Moreover, a full cell based on Zn@MC900 anode and V2 O5 cathode exhibits remarkable rate performance and cycling stability over 3500 cycles. These results establish a structure-mechanism-performance relationship of the carbon host as a highly reversible Zn anode for the reliable operation of ZIBs.

A Novel Method for Effective Cell Segmentation and Tracking in Phase Contrast Microscopic Images.

Cell migration plays an important role in the identification of various diseases and physiological phenomena in living organisms, such as cancer metastasis, nerve development, immune function, wound healing, and embryo formulation and development. The study of cell migration with a real-time microscope generally takes several hours and involves analysis of the movement characteristics by tracking the positions of cells at each time interval in the images of the observed cells. Morphological analysis considers the shapes of the cells, and a phase contrast microscope is used to observe the shape clearly. Therefore, we developed a segmentation and tracking method to perform a kinetic analysis by considering the morphological transformation of cells. The main features of the algorithm are noise reduction using a block-matching 3D filtering method, k-means clustering to mitigate the halo signal that interferes with cell segmentation, and the detection of cell boundaries via active contours, which is an excellent way to detect boundaries. The reliability of the algorithm developed in this study was verified using a comparison with the manual tracking results. In addition, the segmentation results were compared to our method with unsupervised state-of-the-art methods to verify the proposed segmentation process. As a result of the study, the proposed method had a lower error of less than 40% compared to the conventional active contour method.

Characterization of Escherichia coli Strains for Novel Production of Plasmodium ovale Lactate Dehydrogenase.

Malaria is one of the most prevalent diseases worldwide with high incidence and mortality. Among the five species that can infect humans, Plasmodium ovale morphologically resembles Plasmodium vivax, resulting in misidentification and confusion in diagnosis, and is responsible for malarial disease relapse due to the formation of hypnozoites. P. ovale receives relatively less attention compared to other major parasites, such as P. falciparum and P. vivax, primarily due to its lower pathogenicity, mortality rates, and prevalence rates. To efficiently produce lactate dehydrogenase (LDH), a major target for diagnosing malaria, this study used three Escherichia coli strains, BL21(DE3), BL21(DE3)pLysS, and Rosetta(DE3), commonly used for recombinant protein production. These strains were characterized to select the optimal strain for P. ovale LDH (PoLDH) production. Gene cloning for recombinant PoLDH production and transformation of the three strains for protein expression were performed. The optimal PoLDH overexpression and washing buffer conditions in nickel-based affinity chromatography were established to ensure high-purity PoLDH. The yields of PoLDH expressed by the three strains were as follows: BL21(DE3), 7.6 mg/L; BL21(DE3)pLysS, 7.4 mg/L; and Rosetta(DE3), 9.5 mg/L. These findings are expected to be highly useful for PoLDH-specific diagnosis and development of antimalarial therapeutics.

A nystagmus extraction system using artificial intelligence for video-nystagmography.

Benign paroxysmal positional vertigo (BPPV), the most common vestibular disorder, is diagnosed by an examiner changing the posture of the examinee and inducing nystagmus. Among the diagnostic methods used to observe nystagmus, video-nystagmography has been widely used recently because it is non-invasive. A specialist with professional knowledge and training in vertigo diagnosis is needed to diagnose BPPV accurately, but the ratio of vertigo patients to specialists is too high, thus necessitating the need for automated diagnosis of BPPV. In this paper, a convolutional neural network-based nystagmus extraction system, ANyEye, optimized for video-nystagmography data is proposed. A pupil was segmented to track the exact pupil trajectory from real-world data obtained during field inspection. A deep convolutional neural network model was trained with the new video-nystagmography dataset for the pupil segmentation task, and a compensation algorithm was designed to correct pupil position. In addition, a slippage detection algorithm based on moving averages was designed to eliminate the motion artifacts induced by goggle slippage. ANyEye outperformed other eye-tracking methods including learning and non-learning-based algorithms with five-pixel error detection rate of 91.26%.

Ventricle tracking in transesophageal echocardiography (TEE) images during cardiopulmonary resuscitation (CPR) using deep learning and monogenic filtering.

High-quality cardiopulmonary resuscitation (CPR) is the most important factor in promoting resuscitation outcomes; therefore, monitoring the quality of CPR is strongly recommended in current CPR guidelines. Recently, transesophageal echocardiography (TEE) has been proposed as a potential real-time feedback modality because physicians can obtain clear echocardiographic images without interfering with CPR. The quality of CPR would be optimized if the myocardial ejection fraction (EF) could be calculated in real-time during CPR. We conducted a study to derive a protocol to detect systole and diastole automatically and calculate EF using TEE images acquired from patients with cardiac arrest. The data were supplemented using thin-plate spline transformation to solve the problem of insufficient data. The deep learning model was constructed based on ResUNet + + , and a monogenic filtering method was applied to clarify the ventricular boundary. The performance of the model to which the monogenic filter was added and the existing model was compared. The left ventricle was segmented in the ME LAX view, and the left and right ventricles were segmented in the ME four-chamber view. In most of the results, the performance of the model to which the monogenic filter was added was high, and the difference was very small in some cases; but the performance of the existing model was high. Through this learned model, the effect of CPR can be quantitatively analyzed by segmenting the ventricle and quantitatively analyzing the degree of contraction of the ventricle during systole and diastole. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00293-9.

Role of Bicontinuous Structure in Elastomeric Electrolytes for High-Energy Solid-State Lithium-Metal Batteries.

Solid-state lithium (Li)-metal batteries (LMBs) are garnering attention as a next-generation battery technology that can surpass conventional Li-ion batteries in terms of energy density and operational safety under the condition that the issue of uncontrolled Li dendrite is resolved. In this study, various plastic crystal-embedded elastomer electrolytes (PCEEs) are investigated with different phase-separated structures, prepared by systematically adjusting the volume ratio of the phases, to elucidate the structure-property-electrochemical performance relationship of the PCEE in the LMBs. At an optimal volume ratio of elastomer phase to plastic-crystal phase (i.e., 1:1), bicontinuous-structured PCEE, consisting of efficient ion-conducting, plastic-crystal pathways with long-range connectivity within a crosslinked elastomer matrix, exhibits exceptionally high ionic conductivity (≈10-3 S cm-1 ) at 20 °C and excellent mechanical resilience (elongation at break ≈ 300%). A full cell featuring this optimized PCEE, a 35 µm thick Li anode, and a high loading LiNi0.83 Mn0.06 Co0.11 O2 (NMC-83) cathode delivers a high energy density of 437 Wh kganode+cathode+electrolyte -1 . The established structure-property-electrochemical performance relationship of the PCEE for solid-state LMBs is expected to inform the development of the elastomeric electrolytes for various electrochemical energy systems.

A 3D Hierarchical Host with Enhanced Sodiophilicity Enabling Anode-Free Sodium-Metal Batteries.

Sodium-metal batteries (SMBs) are considered as a compliment to lithium-metal batteries for next-generation high-energy batteries because of their low cost and the abundance of sodium (Na). Herein, a 3D nanostructured porous carbon particle containing carbon-shell-coated Fe nanoparticles (PC-CFe) is employed as a highly reversible Na-metal host. PC-CFe has a unique 3D hierarchy based on sub-micrometer-sized carbon particles, ordered open channels, and evenly distributed carbon-coated Fe nanoparticles (CFe) on the surface. PC-CFe achieves high reversibility of Na plating/stripping processes over 500 cycles with a Coulombic efficiency of 99.6% at 10 mA cm-2 with 10 mAh cm-2 in Na//Cu asymmetric cells, as well as over 14 400 cycles at 60 mA cm-2 in Na//Na symmetric cells. Density functional theory calculations reveal that the superior cycling performance of PC-CFe stems from the stronger adsorption of Na on the surface of the CFe, providing initial nucleation sites more favorable to Na deposition. Moreover, the full cell with a PC-CFe host without Na metal and a high-loading Na3 V2 (PO4 )3 cathode (10 mg cm-2 ) maintains a high capacity of 103 mAh g-1 at 1 mA cm-2 even after 100 cycles, demonstrating the operation of anode-free SMBs.

Stimuli-Responsive, Shape-Transforming Nanostructured Particles.

Development of particles that change shape in response to external stimuli has been a long-thought goal for producing bioinspired, smart materials. Herein, the temperature-driven transformation of the shape and morphology of polymer particles composed of polystyrene-b-poly(4-vinylpyridine) (PS-b-P4VP) block copolymers (BCPs) and temperature-responsive poly(N-isopropylacrylamide) (PNIPAM) surfactants is reported. PNIPAM acts as a temperature-responsive surfactant with two important roles. First, PNIPAM stabilizes oil-in-water droplets as a P4VP-selective surfactant, creating a nearly neutral interface between the PS and P4VP domains together with cetyltrimethylammonium bromide, a PS-selective surfactant, to form anisotropic PS-b-P4VP particles (i.e., convex lenses and ellipsoids). More importantly, the temperature-directed positioning of PNIPAM depending on its solubility determines the overall particle shape. Ellipsoidal particles are produced above the critical temperature, whereas convex lens-shaped particles are obtained below the critical temperature. Interestingly, given that the temperature at which particle shape change occurs depends solely on the lower critical solution temperature (LCST) of the polymer surfactants, facile tuning of the transition temperature is realized by employing other PNIPAM derivatives with different LCSTs. Furthermore, reversible transformations between different shapes of PS-b-P4VP particles are successfully demonstrated using a solvent-adsorption annealing with chloroform, suggesting great promise of these particles for sensing, smart coating, and drug delivery applications.