Search

Abstract Purpose To investigate the safety and efficacy of a new micro-controlled radiofrequency device for treatment of conjunctivochalasis (Cch). Methods Data of 127 patients (230 eyes) who underwent ophthalmic radiofrequency treatment for Cch from January 2020 to June 2023 were analyzed retrospectively. Cch coagulation was performed with a radiofrequency electrode tip (OcuRF®, Ilooda, Korea) and a high-frequency radio-wave electric unit (0.6 ~ 0.8 watts, 2 MHz, Acutron™, Ilooda, Korea). Pre- and postoperative Cch grading, slit-lamp photography, tear film break-up time (TBUT), and bulbar conjunctival hyperemia using Keratograph 5 M (Oculus, Wetzlar, Germany) were evaluated. Cch grade 0 or 1 after surgery was regarded as ‘success’. Complications, recurrence, and additional treatment rates were analyzed. Results In 227 (98.7%) eyes, the radiofrequency treatment led to marked improvement of Cch, with 224 (97.4%) eyes achieving grade 0 or 1 at 2 months postoperatively. Eight eyes (3.5%) received additional treatment. TBUT improved from 3.17 ± 0.82 s to 5.28 ± 1.10 s after surgery (P

Abstract Due to their inherent flexibility, solution-processable conjugated polymers are increasingly being considered for the cost-effective production of thin-film semiconductor devices used in Internet of Everything (IoE) applications. With considerable improvements in charge carrier mobilities, the final challenge impeding the commercialization of conjugated polymers may be improving their environmental and electrical stabilities. Recent studies have improved the stability of computing devices (i.e., transistors) by eliminating interface traps and water molecules within conjugated polymers. However, the stability issue of Schottky diodes, which play a crucial role in configuring thin-film IoE devices used in wireless communication and energy harvesting, has been largely overlooked. This study reveals that aluminum, which is commonly used as a cathode metal in polymer Schottky diodes, creates a nonstoichiometric effect when deposited on conjugated polymers, thereby leading to the formation of charge traps over time, which reduces the rectification ratio of the Schottky diodes and induces a significant bias stress effect during operation. To address this issue, we introduce a zinc-oxide sacrificial interlayer between the conjugated polymer and cathode. This interlayer effectively eliminates the penetrated Al metal or ionized Al-induced nonstoichiometric effect without reducing the charge injection efficiency, achieving exceptional environmental and operational stability. The printed polymer Schottky diodes demonstrate consistent rectifying operation at 13.56 MHz for several months with negligible changes in electrical characteristics.

Isolated corneal squamous cell carcinoma without conjunctiva and limbus involvement is rare. We report a case of isolated squamous cell carcinoma in situ of the cornea. A 67-year-old male presented with visual disturbance in the left eye (visual acuity, 0.5), with a lesion isolated to the cornea. After an initial diagnosis of keratitis, he was lost to follow-up for 15 months. He subsequently returned for re-evaluation by a corneal specialist owing to progressive visual impairment in the left eye (visual acuity, 0.2). A left scrape biopsy and histopathological examination revealed squamous cell carcinoma in situ. No systemic evaluation findings of lymphadenopathy or metastasis were observed. Following removal of the residual mass from the cornea, conjunctival tissue samples, including the limbus, were collected for histopathological examination, with negative findings. No recurrence was observed at 15 months postoperatively. Isolated corneal squamous cell carcinoma in situ can be misdiagnosed as simple corneal opacity or keratitis during clinical examination. This condition should be considered in patients with unresponsive keratitis or corneal opacities.

Recently, Lee et al. introduced a novel blind rotation technique utilizing ring automorphisms also known as LMKCDEY. Among known prominent blind rotation methods, LMKCDEY stands out because of its minimal key size and efficient runtime for arbitrary secret keys, although Chillotti et al.’s approach, commonly referred to as CGGI, offers faster runtime when using binary or ternary secrets. In this paper, we propose an enhancement to LMKCDEY’s runtime by incorporating auxiliary keys that encrypt the negated values of secret key elements. Our method not only achieves faster execution than LMKCDEY but also maintains a smaller key size compared to the ternary version of CGGI. Moreover, the proposed technique is compatible with LMKCDEY with only minimal adjustments. Experimental results with OpenFHE demonstrate that our approach can improve bootstrapping runtime by 5–28%, depending on the chosen parameters.

PurposeWe investigated the vertical implantation of a toric implantable collamer lens (ICL) and compared the rotational stability with that of horizontal implantation.MethodsThis matched comparative study retrospectively reviewed and analyzed data from patients who underwent ICL implantation from 2003-2022 by 1:1 matching vertical and horizontal (V and H toric groups, respectively) implantation patients according to preoperative astigmatism, spherical equivalent, sulcus-to-sulcus, anterior chamber depth, and ICL size. Visual acuity, manifest refraction, vaulting, and rotation were measured 3 months postoperatively.ResultsWe included 646 eyes (323 each in the V and H toric groups). No statistically significant difference was observed between groups in postoperative visual acuity, refractive error, and astigmatism. Vaulting was lower in the V toric group. (P < 0.001). The mean lens rotation in the V toric group was less than that in the H toric group (1.11 ± 2.84° versus 3.02 ± 10.34°, P = 0.001). The proportion of eyes in the V and H toric groups showing ≥10° of rotation was 2.5% (8 eyes) and 6.5% (21 eyes), respectively (P = 0.014). Despite repositioning from rotation, three (0.9%) and eight (2.5%) eyes required removal owing to lens re-rotation in the V and H toric groups, respectively.ConclusionToric ICL vertical implantation showed good rotational stability, and appropriate visual acuity correction results with relatively low vaulting. This procedure therefore presents an effective novel method that could replace horizontal toric ICL implantation.

The promising field of homomorphic encryption enables functions to be evaluated on encrypted data and produce results for the same computations done on plaintexts. It, therefore, comes as no surprise that many ventures at constructing homomorphic encryption schemes have come into the limelight in recent years. Most popular are those that rely on the hard lattice problem, called the Ring Learning with Errors problem (RLWE). One major limitation of these homomorphic encryption schemes is that in order to securely increase the maximum multiplicative depth, they need to increase the polynomial-size (degree of the polynomial ring) thereby also ncreasing the complexity of the design. We aim to bridge this gap by proposing a homomorphic encryption (HE) scheme based on the Module Learning with Errors problem (MLWE), ModHE that allows us to break the big computations into smaller ones. Given the popularity of module lattice-based post-quantum schemes, it is an evidently interesting research endeavor to also formulate module lattice-based homomorphic encryption schemes. While our proposed scheme is general, as a case study, we port the well-known RLWE-based CKKS scheme to the MLWE setting. The module version of the scheme completely stops the polynomial-size blowups when aiming for a greater circuit depth. Additionally, it presents greater opportunities for designing flexible, reusable, and parallelizable hardware architecture. A hardware implementation is provided to support our claims. We also acknowledge that as we try to decrease the complexity of computations, the amount of computations (such as relinearizations) increases. We hope that the potential and limitations of using such a hardware-friendly scheme will spark further research.

Spinal manipulation (SM) is a common manual therapy technique; however, there is limited knowledge regarding the coordination of hand and foot forces during SM. This study investigated the biomechanics of force transmission and generation in the hands and feet of a single therapist who performed pelvic SM on 45 healthy subjects. Two force plates were used to measure the ground reaction forces (GRF) from the feet, and one controller was used to measure the contact hand forces (CHF). The results showed that foot force preceded hand force and that the foot and hand exhibited opposing patterns of force variation. The CHF peak was positively correlated with the CH preload maximum and minimum forces and negatively correlated with the GRF run-down. These findings suggested that the therapist used a coordinated strategy of avoiding weight support with the feet and supporting the weight with the hands to amplify the thrust force. This study provides new insights into the biomechanics of SM and has implications for teaching, motor learning, and safety.

We aimed to investigate the diagnostic accuracy of Screening Corneal Objective Risk of Ectasia (SCORE) Analyzer software using ANTERION, a swept-source optical coherence tomography device, for keratoconus diagnosis in an Asian population. A total of 151 eyes of 151 patients were included in this retrospective study as follows: 60, 45, and 46 keratoconus, keratoconus suspects, and normal control eyes, respectively. Parameters in the SCORE calculation, including six indices, were compared for the three groups. The receiver operating characteristic curve analysis and cut-off value were estimated to assess the diagnostic ability to differentiate keratoconus and keratoconus suspect eyes from the normal group. The SCORE value and six indices were significantly correlated—“AntK max” (R = 0.864), “AntK oppoK” (R = 0.866), “Ant inf supK” (R = 0.943), “Ant irre 3mm” (R = 0.741), “post elevation at the thinnest point” (R = 0.943), and “minimum corneal thickness” (R = −0.750). The SCORE value showed high explanatory power (98.1%), sensitivity of 81.9%, and specificity of 78.3% (cut-off value: 0.25) in diagnosing normal eyes from the keratoconus suspect and keratoconus eyes. The SCORE Analyzer was found to be valid and consistent, showing good sensitivity and specificity for keratoconus detection in an Asian population.

Fully homomorphic encryption (FHE) is a prospective tool for privacy-preserving machine learning (PPML). Several PPML models have been proposed based on various FHE schemes and approaches. Although FHE schemes are suitable as tools for implementing PPML models, previous PPML models based on FHE, such as CryptoNet, SEALion, and CryptoDL, are limited to simple and nonstandard types of machine learning models; they have not proven to be efficient and accurate with more practical and advanced datasets. Previous PPML schemes replaced non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and did not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could neither use standard activation functions nor employ large numbers of layers. In this work, we first implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and plaintext model parameters. Instead of replacing the non-arithmetic functions with simple arithmetic functions, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as ReLU and Softmax, with sufficient precision. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate an arbitrary deep learning model on encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.43% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 92.43%±2.65%, which is quite close to that of the original ResNet-20 CNN model (91.89%). It takes approximately 3 h for inference on a dual Intel Xeon Platinum 8280 CPU (112 cores) with 172 GB of memory. We believe that this opens the possibility of applying FHE to an advanced deep PPML model.

PurposeTo evaluate the changes of the corneal epithelial thickness (ET) profile induced by combined small incision lenticule extraction and accelerated corneal collagen crosslinking (SMILE-xtra) for myopia compared with the standard small incision lenticule extraction (SMILE).SettingNuri Eye Hospital, 61, Dunsan-ro, Seo-gu, Daejeon, 35233, Korea.DesignRetrospective cross-sectional study.MethodsThirty-one myopic eye undergoing SMILE-xtra and control group of 36 myopic eyes undergoing SMILE were retrospectively analyzed. Spectral-domain optical coherence tomography (CIRRUS™ HD-OCT 5000, ZEISS, Dublin, CA) was used to measure corneal ET of 17 zones within the central 7-mm zone at preoperative, postoperative 1 month, 3 months and 6 months. Postoperative ET alterations were analyzed for correlation with treatment parameters.ResultsThere was no difference in preoperative mean age, postoperative MRSE, visual acuity, and ablation depth between the two groups, and there was a significant difference in preoperative central corneal thickness. Both groups showed the greatest increase in corneal ET in the paracentral area on the inferotemporal area, respectively, for 6 months. The preoperative MRSE and the ablation depth showed significant correlation with the postoperative epithelial thickening in mid-peripheral sectors in both groups, and significant negative correlations in paracentral sectors only in SMILE-xtra group.ConclusionsIt is significant as the first study to compare corneal epithelial remodeling between SMILE and SMILE with accelerated corneal collagen crosslinking. The SMILE-xtra with the relatively large corneal ablation did not show a significant difference in the pattern of corneal epithelial remodeling compared to the SMILE group.

Homomorphic encryption enables computation on encrypted data, and hence it has a great potential in privacy-preserving outsourcing of computations to the cloud. Hardware acceleration of homomorphic encryption is crucial as software implementations are very slow. In this paper, we present design methodologies for building a programmable hardware accelerator for speeding up the cloud-side homomorphic evaluations on encrypted data. First, we propose a divide-and-conquer technique that enables homomorphic evaluations in the polynomial ring RQ,2N = ZQ[x]/(x2N + 1) to use a hardware accelerator that has been built for the smaller ring RQ,N = ZQ[x]/(xN + 1). The technique makes it possible to use a single hardware accelerator flexibly for supporting several homomorphic encryption parameter sets. Next, we present several architectural design methods that we use to realize the flexible and instruction-set accelerator architecture, which we call ‘Medha’. At every level of the implementation hierarchy, we explore possibilities for parallel processing. Starting from hardware-friendly parallel algorithms for the basic building blocks, we gradually build heavily parallel RNS polynomial arithmetic units. Next, many of these parallel units are interconnected elegantly so that their interconnections require the minimum number of nets, therefore making the overall architecture placement-friendly on the platform. As homomorphic encryption is computation- as well as data-centric, the speed of homomorphic evaluations depends greatly on the way the data variables are handled. For Medha, we take a memory-conservative design approach and get rid of any off-chip memory access during homomorphic evaluations. Finally, we implement Medha in a Xilinx Alveo U250 FPGA and measure timing performances of the microcoded homomorphic addition, multiplication, key-switching, and rescaling routines for the leveled fully homomorphic encryption scheme RNSHEAAN at 200 MHz clock frequency. For the large parameter sets (log Q,N) = (438, 214) and (546, 215), Medha achieves accelerations by up to 68× and 78× times respectively compared to a highly optimized software implementation Microsoft SEAL running at 2.3 GHz.

As a new type of one-dimensional nanomaterial, carbon nanotubes (CNTs) have been used as a chemical sensing material due to their excellent electrical, mechanical and chemical properties. Several recent studies have attempted to obtain an electrochemical sensor based on CNTs, and CNT-based humidity sensors have potential applications in industrial, agricultural and medical fields and in smart wearable electronic devices. Although various CNT-based humidity sensors have been reported, in this work, we investigated the humidity effects in depth and the underlying mechanisms according to CNT type, i.e., semiconducting (s-CNT) and metallic (m-CNT) CNTs, which is determined by the chiral vector during CNT growth. For this purpose, we fabricated CNT-based humidity sensors with highly purified, solution-processed 99% single-walled s-CNT and m-CNT network films bridged by palladium (Pd) electrodes. The fabricated sensors exhibited completely different humidity responses, as denoted by the film resistance as a function of the relative humidity (RH), according to the CNT type. In the s-CNT-based sensor, the resistance tended to decrease as the RH increased, while the m-CNT-based sensor showed the opposite tendency. Based on these results, a humidity sensing mechanism according to the CNT type was proposed in this work. We believe that our findings can serve as design guidelines for CNT-based humidity sensors.

Although the intrinsic variability in nanoelectronic devices has been a major obstacle and has prevented mass production, this natural stochasticity can be an asset in hardware security applications. Herein, we demonstrate a true random number generator (TRNG) based on stochastic carrier trapping/detrapping processes in randomly distributed carbon nanotube networks. The bitstreams collected from the TRNG passed all the National Institute of Standards and Technology randomness tests without post-processing. The random bit generated in this study is sufficient for encryption applications, particularly those related to the Internet of Things and edge computing, which require significantly lower power consumption.

Many lattice-based cryptographic schemes are constructed based on hard problems on an algebraic structured lattice, such as the short integer solution (SIS) problems. These problems are called ring-SIS (R-SIS) and its generalized version, module-SIS (M-SIS). Generally, it has been considered that problems defined on the module lattice are more difficult than the problems defined on the ideal lattice. However, Koo, No, and Kim showed that R-SIS is more difficult than M-SIS under some norm constraints of R-SIS. However, this reduction has problems in that the rank of the module is limited to about half of the instances of R-SIS, and the comparison is not performed through the same modulus of R-SIS and M-SIS. In this paper, we propose the three reductions. First, we show that R-SIS is more difficult than M-SIS with the same modulus and ring dimension under some constraints of R-SIS. Also, we show that through the reduction from M-SIS to R-SIS with the same modulus, the rank of the module is extended as much as the number of instances of R-SIS from half of the number of instances of R-SIS compared to the previous work. Second, we show that R-SIS is more difficult than M-SIS under some constraints, which is tighter than the M-SIS in the previous work. Finally, we propose that M-SIS with the modulus prime $q^{k}$ is more difficult than M-SIS with the composite modulus $c$ , such that $c$ is divided by $q$ . Through the three reductions, we conclude that R-SIS with the modulus $q$ is more difficult than M-SIS with the composite modulus $c$ .

Background We investigated the safety and feasibility of ultrasound-guided peripherally inserted central venous catheter (PICC) placements performed by intensive care medical trainees in comparison to PICC placements performed by intensivists. Methods This was a retrospective and observational study of adult patients who underwent PICC placement and were admitted to the intensive care unit (ICU) between July 2013 and March 2018. Ultrasound-guided PICC was performed at the bedside by an intensivist or intensive care medical trainee if intrahospital transport was inappropriate. The primary endpoint was PICC-induced complications. The secondary endpoint was initial success of PICC. Results A total of 209 patients underwent PICC placement during the study period. There were no significant differences in age, sex, body mass index, comorbidities, causes of ICU admission, or severity scores between the trainee-led PICC and intensivist-led PICC groups. Difficult venous access (42.6%) and requirement for central line infusion (39.2%) were the most common reasons for PICC placement. The basilic vein (62.2%) was the most common target vein among patients who underwent PICC. There were no significant differences in complications between the two groups (P=0.473). In addition, the initial success rate and procedural time were similar between the two groups (P=0.108 and P=0.076, respectively). There were no insertional injuries and moderate or severe bleeding in patients with PICC. Conclusion Ultrasound-guided PICC placement by an intensive care medical trainee may be safe and feasible compared to PICC placement by an intensivist.

With the development of depth sensors and skeleton tracking algorithms, many skeleton-based pathological gait classification methods have recently been proposed. However, these methods classify only simple gait patterns, and there is no approach to classify complicated gait patterns. In this paper, we classify 1 normal and 5 pathological gaits (antalgic, stiff-legged, lurching, steppage, and Trendelenburg gaits) by using a gated recurrent unit (GRU)-based classifier and 3D skeleton data. We collected skeleton datasets for 1 normal and 5 pathological gaits by using a multiperspective Kinect system. We developed the GRU classifier to classify the pathological gaits and compared its performance with that of other machine learning-based classifiers. Furthermore, we considered various joint groups to identify important and irrelevant joints for pathological gait classification and to improve the performance of the GRU classifier. When all skeleton data are used, the GRU classifier achieves a classification accuracy of 90.13%. A long short-term memory (LSTM)-based classifier achieves the next highest accuracy of 87.25%. The classification accuracy of the GRU classifier depends on the joint groups considered, and the classification accuracy increases to 93.67% when only leg joints are considered. This study indicates that various pathological gaits can be classified by using skeleton data and the GRU classifier. The proposed method can be used to support medical and clinical decisions. Furthermore, the results for various joint groups can be used to develop other methods in cases where only specific joint data are available because of environmental limitations.

Resistive crossbar arrays can carry out energy-efficient vector-matrix multiplication, which is a crucial operation in most machine learning applications. However, practical computing tasks that require high precision remain challenging to implement in such arrays because of intrinsic device variability. Herein, we experimentally demonstrate a precision-extension technique whereby high precision can be attained through the combined operation of multiple devices, each of which stores a portion of the required bit width. Additionally, designed analog-to-digital converters are used to remove the unpredictable effects from noise sources. An 8 × 15 carbon nanotube transistor array can perform multiplication operation, where operands have up to 16 valid bits, without any error, making in-memory computing approaches attractive for high-throughput energy-efficient machine learning accelerators.

We present a novel code-based signature scheme called modified pqsigRM. This scheme is based on a modified Reed-Muller (RM) code, which reduces the signing complexity and key size compared with existing code-based signature schemes. In fact, it strengthens pqsigRM submitted to NIST for post-quantum cryptography standardization. The proposed scheme has the advantage of the pqsigRM decoder and uses public codes that are more difficult to distinguish from random codes. We use (U, U + V)-codes with the high-dimensional hull to overcome the disadvantages of code-based schemes. The proposed decoder samples from coset elements with small Hamming weight for any given syndrome and efficiently finds such an element. Using a modified RM code, the proposed signature scheme resists various known attacks on RM-code-based cryptography. For 128 bits of classical security, the signature size is 4096 bits, and the public key size is less than 1 MB.

Since Cheon et al. introduced an approximate homomorphic encryption scheme for complex numbers called Cheon-Kim-Kim-Song (CKKS) scheme, it has been widely used and applied in real-life situations, such as privacy-preserving machine learning. The polynomial approximation of a modulus reduction is the most difficult part of the bootstrapping for the CKKS scheme. In this article, we cast the problem of finding an approximate polynomial for a modulus reduction into an L2-norm minimization problem. As a result, we find an approximate polynomial for the modulus reduction without using the sine function, which is the upper bound for the approximation of the modulus reduction. With the proposed method, we can reduce the degree of the polynomial required for an approximate modulus reduction, while also reducing the error compared with the most recent result reported by Han et al. (CT-RSA’ 20). Consequently, we can achieve a low-error approximation, such that the maximum error is less than 2−40 for the size of the message $m/q\approx 2^{-10}$ . By using the proposed method, the constraint of $q = \mathcal {O}(m^{3/2})$ is relaxed as $\mathcal {O}(m)$ , and thus the level loss in bootstrapping can be reduced. The solution to the cast problem is determined in an efficient manner without iteration.

Abstract In the biological neural network, the learning process is achieved through massively parallel synaptic connections between neurons that can be adjusted in an analog manner. Recent developments in emerging synaptic devices and their networks can emulate the functionality of a biological neural network, which will be the fundamental building block for a neuromorphic computing architecture. However, on-chip implementation of a large-scale artificial neural network is still very challenging due to unreliable analog weight modulation in current synaptic device technology. Here, we demonstrate a binarized neural network (BNN) based on a gate-all-around silicon nanosheet synaptic transistor, where reliable digital-type weight modulation can contribute to improve the sustainability of the entire network. BNN is applied to three proof-of-concept examples: (1) handwritten digit classification (MNIST dataset), (2) face image classification (Yale dataset), and (3) experimental 3 × 3 binary pattern classifications using an integrated synaptic transistor network (total 9 × 9 × 2 162 cells) through a supervised online training procedure. The results consolidate the feasibility of binarized neural networks and pave the way toward building a reliable and large-scale artificial neural network by using more advanced conventional digital device technologies.

The autonomous flight of an unmanned aerial vehicle refers to creating a new flight route after self-recognition and judgment when an unexpected situation occurs during the flight. The unmanned aerial vehicle can fly at a high speed of more than 60 km/h, so obstacle recognition and avoidance must be implemented in real-time. In this paper, we propose to recognize objects quickly and accurately by effectively using the H/W resources of small computers mounted on industrial unmanned air vehicles. Since the number of pixels in the image decreases after the resizing process, filtering and object resizing were performed according to the altitude, so that quick detection and avoidance could be performed. To this end, objects up to 60 m in height were classified by subdividing them at 20 m intervals, and objects unnecessary for object detection were filtered with deep learning methods. In the 40 m to 60 m sections, the average speed of recognition was increased by 38%, without compromising the accuracy of object detection.

Solution-processed carbon nanotubes (CNTs) have recently attracted significant attention as p-type thin-film transistor (TFT) channels due to their high carrier mobility, high uniformity, and low process temperature. However, implementing sophisticated macroelectronics with a combination of single CNT-TFTs has been challenging because it is difficult to fabricate n-type CNT-TFTs. Therefore, in combination with indium-gallium-zinc-oxide (IGZO), which has excellent electrical performance and has been commercialized as an n-type oxide TFT, we demonstrated various hybrid complementary metal-oxide semiconductor integrated circuits, such as inverters and nor and nand gates. This hybrid integration approach allows us to combine the strength of p-type CNT- and n-type IGZO-TFTs, thus offering a significant improvement for macroelectronic applications.

The demand for transparent and flexible electronic devices, which are an emerging technology for the next generation of sensors, continues to grow in both applications and development due to their potential to make a significant commercial impact in a wide variety of areas. Here, we demonstrate a highly transparent tactile sensor with 92% optical transparency in the visible range based on solution-processed 99% metallic CNTs attached on a polydimethylsiloxane (PDMS) film. We efficiently reconstructed the pressed, stimulated spatial location by increasing the injection current (Iinjection) during electrical resistance tomography (ERT) that computed the internal two-dimensional (2-D) resistivity distribution.

High theoretical capacity and low-cost copper sulfide (CuxS)-based anodes have gained great attention for advanced sodium-ion batteries (SIBs). However, their practical application may be hindered due to their unstable cycling performance and problems with the dissolution of sodium sulfides (NaxS) into electrolyte. Here, we employed metal organic framework (MOF-199) as a sacrificial template to fabricate nanoporous CuxS with a large surface area embedded in the MOF-derived carbon network (CuxS-C) through a two-step process of sulfurization and carbonization via H2S gas-assisted plasma-enhanced chemical vapor deposition (PECVD) processing. Subsequently, we uniformly coated a nanocarbon layer on the Cu1.8S-C through hydrothermal and subsequent annealing processes. The physico-chemical properties of the nanocarbon layer were revealed by the analytical techniques of high-resolution transmission electron microscopy (HRTEM), energy-dispersive X-ray spectroscopy (EDS), and scanning electron microscopy (SEM). We acquired a higher SIB performance (capacity retention (~93%) with a specific capacity of 372 mAh/g over 110 cycles) of the nanoporous Cu1.8S-C/C core/shell anode materials than that of pure Cu1.8S-C. This encouraging SIB performance is attributed to the key roles of a nanocarbon layer coated on the Cu1.8S-C to accommodate the volume variation of the Cu1.8S-C anode structure during cycling, enhance electrical conductivity and prevent the dissolution of NaxS into the electrolyte. With these physico-chemical and electrochemical properties, we ensure that the Cu1.8S-C/C structure will be a promising anode material for large-scale and advanced SIBs.