Freeform metasurface color router for deep submicron pixel image sensors.

Advances in imaging technologies have led to a high demand for ultracompact, high-resolution image sensors. However, color filter-based image sensors, now miniaturized to deep submicron pixel sizes, face challenges such as low signal-to-noise ratio due to fewer photons per pixel and inherent efficiency limitations from color filter arrays. Here, we demonstrate a freeform metasurface color router that achieves ultracompact pixel sizes while overcoming the efficiency limitations of conventional architectures by splitting and focusing visible light instead of filtering. This development is enabled by a fully differentiable topology optimization framework to maximize the use of the design space while ensuring fabrication feasibility and robustness to fabrication errors. The metasurface can distribute an average of 85% of incident visible light according to the Bayer pattern with a pixel size of 0.6 µm. The device and design methodology enable the compact, high-sensitivity, and high-resolution image sensors for various modern technologies and pave the way for the advanced photonic device design.

Additive-Driven Nanoscale Architecture of Solid Electrolyte Interphase Revealed by Cryogenic Transmission Electron Microscopy.

In Li metal batteries (LMBs), which boast the highest theoretical capacity, the chemical structure of the solid electrolyte interphase (SEI) serves as the key component that governs the growth of reactive Li. Various types of additives have been developed for electrolyte optimization, representing one of the most effective strategies to enhance the SEI properties for stable Li plating. However, as advanced electrolyte systems become more chemically complicated, the use of additives is empirically optimized. Indeed, their role in SEI formation and the resulting cycle life of LMBs are not well-understood. In this study, we employed cryogenic transmission electron microscopy combined with Raman spectroscopy, theoretical studies including molecular dynamics (MD) simulations and density functional theory (DFT) calculations, and electrochemical measurements to explore the nanoscale architecture of SEI modified by the most representative additives, lithium nitrate (LiNO3) and vinylene carbonate (VC), applied in a localized high-concentration electrolyte. We found that LiNO3 and VC play distinct roles in forming the SEI, governing the solvation structure, and influencing the kinetics of electrochemical reduction. Their collaboration leads to the desired SEI, ensuring prolonged cycle performance for LMBs. Moreover, we propose mechanisms for different Li growth and cycling behaviors that are determined by the physicochemical properties of SEI, such as uniformity, elasticity, and ionic conductivity. Our findings provide critical insights into the appropriate use of additives, particularly regarding their chemical compatibility.

Nonvolatile Reconfigurable Logic Device Based on Photoinduced Interfacial Charge Trapping in van der Waals Gap.

Due to the increasing complexity in miniaturization of electronic devices, reconfigurable field-effect transistors (RFETs) have emerged as a solution. Although the foundational concepts of RFETs have matured over two decades, ongoing breakthroughs are needed to address challenges such as improving the device performance as well as achieving balanced symmetry between n-type and p-type transport modes with long-term stability. Herein, we present a nonvolatile WSe2-based RFET that utilizes photoassisted interfacial charge trapping at the h-BN and SiO2 interface. Unlike typical RFETs with two gate electrodes, our RFETs achieved polarity control with a single operating gate activated exclusively under white-light exposure. The threshold voltage was tunable, ranging from 27.4 (-31.6 V) to 0.9 (+19.5 V), allowing selective activation of n-type (p-type) operation at VGS = 0 V. Additionally, our WSe2-based RFETs show superior repeatability and long-term stability. Leveraging these advantages, various reconfigurable logic circuits were successfully demonstrated, including complementary inverters and switch circuits as well as pull-up and pull-down circuits, highlighting the potential of WSe2 FETs for future advancements of integrated circuits.

Donor body mass index over 30 is no barrier for pure laparoscopic donor right hepatectomy.

Backgrounds/Aims: Challenges arise when translating pure laparoscopic donor right hepatectomy (PLDRH) results from Asian to Western donors, due to differences in body mass index (BMI). This study compares the outcomes of PLDRH and conventional open donor right hepatectomy (CDRH) in donors with BMI over 30. Methods: Medical records of live liver donors (BMI > 30) undergoing right hepatectomy (2010-2021) were compared: 25 PLDRH cases vs. 19 CDRH cases. Donor and recipient demographics, operative details, and outcomes were analyzed. Results: PLDRH and CDRH had similar donor and recipient characteristics. PLDRH had longer liver removal and warm ischemic times, but a shorter post-liver removal duration than CDRH. Donor complication rates were comparable, with the highest complication being grade IIIa in PLDRH, necessitating needle aspiration for biloma on postoperative day 11. Fortunately, this donor fully recovered without additional treatment. No complications exceeding Clavien-Dindo grade IIIa occurred in either group. Recipient outcomes between the groups were similar. Conclusions: This study supports PLDRH as a viable option for donors with BMI over 30, challenging the notion that high BMI should deter considering PLDRH. The findings provide valuable insights into the safety and feasibility of PLDRH, encouraging further exploration of this technique in diverse donor populations.

Sensor-Based Indoor Fire Forecasting Using Transformer Encoder.

Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios.

Impact of Preformed Donor-specific Antibodies in Comparison to ABO Incompatibility in Living Donor Liver Transplantation: A Propensity Score-Matched Analysis.

BACKGROUND: Immunological factors play a pivotal role in the outcomes of solid organ transplantation. We aimed to elucidate the effects of donor-specific antibodies (DSAs) and ABO compatibility on living donor liver transplantation (LDLT) outcomes. METHODS: A retrospective analysis was conducted on 584 LDLT recipients from 2015 to 2020. The recipients were stratified into 3 groups: ABO-compatible recipients without DSAs (group 1), ABO-compatible recipients with DSAs (group 2), and ABO-incompatible recipients without DSAs (group 3). Propensity score matching was used for balanced comparisons. RESULTS: In the matched comparisons, group 2 exhibited a higher incidence of T cell-mediated rejection compared with group 1 (22.7% versus 4.5%, P = 0.030). Despite this, the 5-y survival rates were similar between groups 1 and 2 (81.6% versus 95.5%, P = 0.085). Group 3, in comparison with group 1, showed elevated rates of cytomegalovirus infection (23.2% versus 7.3%, P = 0.008), T cell-mediated rejection (28.0% versus 7.3%, P = 0.001), and antibody-mediated rejection (13.4% versus 0%, P = 0.001). However, the survival rates were comparable between group 3 and group 1 (82.0% versus 86.5%, P = 0.220, respectively). Comparisons between group 2 and group 3 did not reveal significant differences in postoperative outcomes or survival rates (P > 0.05). CONCLUSIONS: DSA positivity and ABO incompatibility contribute to distinct posttransplant complications in LDLT. The integrated consideration of both factors in pretransplant assessment may enhance risk stratification and inform tailored interventions. Further research is required to corroborate these findings and provide mechanistic insights.

Energy Storage Performance of Electrode Materials Derived from Manganese Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) are porous materials assembled using metal and organic linkers, showing a high specific surface area and a tunable pore size. Large portions of metal open sites in MOFs can be exposed to electrolyte ions, meaning they have high potential to be used as electrode materials in energy storage devices such as supercapacitors. Also, they can be easily converted into porous metal oxides by heat treatment. In this study, we obtained high energy storage performance by preparing electrode materials through applying heat treatment to manganese MOFs (Mn-MOFs) under air. The chemical and structural properties of synthesized and thermally treated Mn-MOFs were measured by Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). The surface area and porosity were investigated by nitrogen adsorption/desorption isotherms. The electrochemical properties were studied by cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) using a three-electrode cell. It was found that Mn-MOF electrodes that underwent heat treatment at 400 °C under air consisted of Mn2O3 with high specific surface area and porosity. They also showed a superior specific capacitance of 214.0 F g-1 and an energy density value of 29.7 Wh kg-1 (at 0.1 A g-1) compared to non-treated Mn-MOFs.

High-Throughput Manufacturing of Multimodal Epidermal Mechanosensors with Superior Detectability Enabled by a Continuous Microcracking Strategy.

Non-invasive human-machine interactions (HMIs) are expected to be promoted by epidermal tactile receptive devices that can accurately perceive human activities. In reality, however, the HMI efficiency is limited by the unsatisfactory perception capability of mechanosensors and the complicated techniques for device fabrication and integration. Herein, a paradigm is presented for high-throughput fabrication of multimodal epidermal mechanosensors based on a sequential "femtosecond laser patterning-elastomer infiltration-physical transfer" process. The resilient mechanosensor features a unique hybrid sensing layer of rigid cellular graphitic flakes (CGF)-soft elastomer. The continuous microcracking of CGF under strain enables a sharp reduction in conductive pathways, while the soft elastomer within the framework sustains mechanical robustness of the structure. As a result, the mechanosensor achieves an ultrahigh sensitivity in a broad strain range (GF of 371.4 in the first linear range of 0-50%, and maximum GF of 8922.6 in the range of 61-70%), a low detection limit (0.01%), and a fast response/recovery behavior (2.6/2.1 ms). The device also exhibits excellent sensing performances to multimodal mechanical stimuli, enabling high-fidelity monitoring of full-range human motions. As proof-of-concept demonstrations, multi-pixel mechanosensor arrays are constructed and implemented in a robot hand controlling system and a security system, providing a platform toward efficient HMIs.

Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing.

The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device's signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.

Open-Air Testing of Dual-Comb Time-of-Flight Measurement.

We configured a long-distance ranging apparatus to test the principle of dual-comb time-of-flight measurement using ultrashort lasers. Emphasis was given to the evaluation of open-air performance quantitatively in terms of the measurement resolution and stability. The test results revealed that our dual-comb asynchronous optical pulse sampling permits micrometer-resolved ranging with a repeatability of 2.05 µm over a 648 m distance in dry weather conditions. Further atmospheric effects were evaluated in three different weather conditions with corresponding Allan deviations. Finally, the capability of simultaneous determination of multiple targets was verified with the potential of advanced industrial applications, such as manufacturing, surveying, metrology, and geodesy.

Experimental Study on Effects of CO2 Curing Conditions on Mechanical Properties of Cement Paste Containing CO2 Reactive Hardening Calcium Silicate Cement.

Human survival is threatened by the rapid climate change due to global warming caused by the increase in CO2 emissions since the Second Industrial Revolution. This study developed a secondary cement product production technology by replacing cement, a conventional binder, with calcium silicate cement (CSC), i.e., CO2 reactive hardening cement, to reduce CO2 emissions and utilize CO2 from the cement industry, which emits CO2 in large quantities. Results showed that the carbonation depth, compressive strength increase rate, and CO2 sequestration rate increased as the CSC content increased, suggesting that CSC can be applied as a secondary cement product.

The Study of an Adaptive Bread Maker Using Machine Learning.

Bread is one of the most consumed foods in the world, and modern food processing technologies using artificial intelligence are crucial in providing quality control and optimization of food products. An integrated solution of sensor data and machine learning technology was determined to be appropriate for identifying real-time changing environmental variables and various influences in the baking process. In this study, the Baking Process Prediction Model (BPPM) created by data-based machine learning showed excellent performance in monitoring and analyzing real-time sensor and vision data in the baking process to predict the baking stages by itself. It also has the advantage of improving the quality of bread. The volumes of bread made using BPPM were 127.54 ± 2.54, 413.49 ± 2.59, 679.96 ± 1.90, 875.79 ± 2.46, and 1260.70 ± 3.13, respectively, which were relatively larger than those made with fixed baking time (p < 0.05). The developed system is evaluated to have great potential to improve precision and efficiency in the food production and processing industry. This study is expected to lay the foundation for the future development of artificial intelligence and the food industry.

Multimodal E-Textile Enabled by One-Step Maskless Patterning of Femtosecond-Laser-Induced Graphene on Nonwoven, Knit, and Woven Textiles.

Personal wearable devices are considered important in advanced healthcare, military, and sports applications. Among them, e-textiles are the best candidates because of their intrinsic conformability without any additional device installation. However, e-textile manufacturing to date has a high process complexity and low design flexibility. Here, we report the direct laser writing of e-textiles by converting raw Kevlar textiles to electrically conductive laser-induced graphene (LIG) via femtosecond laser pulses in ambient air. The resulting LIG has high electrical conductivity and chemical reliability with a low sheet resistance of 2.86 Ω/□. Wearable multimodal e-textile sensors and supercapacitors are realized on different types of Kevlar textiles, including nonwoven, knit, and woven structures, by considering their structural textile characteristics. The nonwoven textile exhibits high mechanical stability, making it suitable for applications in temperature sensors and micro-supercapacitors. On the other hand, the knit textile possesses inherent spring-like stretchability, enabling its use in the fabrication of strain sensors for human motion detection. Additionally, the woven textile offers special sensitive pressure-sensing networks between the warp and weft parts, making it suitable for the fabrication of bending sensors used in detecting human voices. This direct laser synthesis of arbitrarily patterned LIGs from various textile structures could result in the facile realization of wearable electronic sensors and energy storage.

The Role and Clinical Outcomes of Endoscopic Spine Surgery of Treating Spinal Metastases; Outcomes of 29 Cases From 8 Countries.

OBJECTIVE: We aim to report the outcomes and feasibility of endoscopic spine surgery used to treat symptomatic spinal metastases patients. This is the most extensive series of spinal metastases patients who underwent endoscopic spine surgery. METHODS: A worldwide collaborative network group of endoscopic spine surgeons, named 'ESSSORG,' was established. Patients diagnosed with spinal metastases who underwent endoscopic spine surgery from 2012 to 2022 were retrospectively reviewed. All related patient data and clinical outcomes were gathered and analyzed before the surgery and the followtime period of 2 weeks, 1 month, 3 months, and 6 months. RESULTS: A total of 29 patients from South Korea, Thailand, Taiwan, Mexico, Brazil, Argentina, Chile, and India, were included. The mean age was 59.59 years, and 11 of them were female. The total number of decompressed levels was 40. The technique was relatively equal (15 uniportal; 14 biportal). The average length of admission was 4.41 days. Of all patients with an American Spinal Injury Association Impairment Scale of D or lower before surgery, 62.06% reported having at least one recovery grade after the surgery. Almost all clinical outcomes parameters statistically significantly improved and maintained from 2 weeks to 6 months after the surgery. Few surgical-related complications (4 cases) were reported. CONCLUSION: Endoscopic spine surgery is a valid option for treating spinal metastases patients as it could yield comparable results to other minimally invasive spine surgery techniques. As the aim is to improve the quality of life, this procedure is valuable and holds value in palliative oncologic spine surgery.

Phase-Locked Synthetic Wavelength Interferometer Using a Femtosecond Laser for Absolute Distance Measurement without Cyclic Error.

We present a phase-locked synthetic wavelength interferometer that enables a complete elimination of cyclic errors in absolute distance measurements. With this method, the phase difference between the reference and measurement paths is fed back into a phase lock-in system, which is then used to control the synthetic wavelength and set the phase difference to zero using an external cavity acousto-optic modulator. We validated the cyclic error removal of the proposed phase-locked method by comparing it with the conventional phase-measuring method of the synthetic wavelength interferometer. By analyzing the locked error signal, we achieved a precision of 0.6 mrad in phase without any observed cyclic errors.

Ultra-thin light-weight laser-induced-graphene (LIG) diffractive optics.

The realization of hybrid optics could be one of the best ways to fulfill the technological requirements of compact, light-weight, and multi-functional optical systems for modern industries. Planar diffractive lens (PDL) such as diffractive lenses, photonsieves, and metasurfaces can be patterned on ultra-thin flexible and stretchable substrates and be conformally attached on top of arbitrarily shaped surfaces. In this review, we introduce recent research works addressed to the design and manufacturing of ultra-thin graphene optics, which will open new markets in compact and light-weight optics for next-generation endoscopic brain imaging, space internet, real-time surface profilometry, and multi-functional mobile phones. To provide higher design flexibility, lower process complexity, and chemical-free process with reasonable investment cost, direct laser writing (DLW) of laser-induced-graphene (LIG) is actively being applied to the patterning of PDL. For realizing the best optical performances in DLW, photon-material interactions have been studied in detail with respect to different laser parameters; the resulting optical characteristics have been evaluated in terms of amplitude and phase. A series of exemplary laser-written 1D and 2D PDL structures have been actively demonstrated with different base materials, and then, the cases are being expanded to plasmonic and holographic structures. The combination of these ultra-thin and light-weight PDL with conventional bulk refractive or reflective optical elements could bring together the advantages of each optical element. By integrating these suggestions, we suggest a way to realize the hybrid PDL to be used in the future micro-electronics surface inspection, biomedical, outer space, and extended reality (XR) industries.

Comparison of Newly Proposed LDL-Cholesterol Estimation Equations.

BACKGROUND: Low-density lipoprotein cholesterol is an important marker highly associated with cardiovascular disease. Since the direct measurement of it is inefficient in terms of cost and time, it is common to estimate through the Friedewald equation developed about 50 years ago. However, various limitations exist since the Friedewald equation was not designed for Koreans. This study proposes a new low-density lipoprotein cholesterol estimation equation for South Koreans using nationally approved statistical data. METHODS: This study used data from the Korean National Health and Nutrition Examination Survey from 2009 to 2019. The 18,837 subjects were used to develop the equation for estimating low-density lipoprotein cholesterol. The subjects included individuals with low-density lipoprotein cholesterol levels directly measured among those with high-density lipoprotein cholesterol, triglycerides, and total cholesterol measured. We compared twelve equations developed in the previous studies and the newly proposed equation (model 1) developed in this study with the actual low-density lipoprotein cholesterol value in various ways. RESULTS: The low-density lipoprotein cholesterol value estimated using the estimation formula and the actual low-density lipoprotein cholesterol value were compared using the root mean squared error. When the triglyceride level was less than 400 mg/dL, the root mean squared of the model 1 was 7.96, the lowest compared to other equations, and the model 2 was 7.82. The degree of misclassification was checked according to the NECP ATP III 6 categories. As a result, the misclassification rate of the model 1 was the lowest at 18.9%, and Weighted Kappa was the highest at 0.919 (0.003), which means it significantly reduced the underestimation rate shown in other existing estimation equations. Root mean square error was also compared according to the change in triglycerides level. As the triglycerides level increased, the root mean square error showed an increasing trend in all equations, but it was confirmed that the model 1 was the lowest compared to other equations. CONCLUSION: The newly proposed low-density lipoprotein cholesterol estimation equation showed significantly improved performance compared to the 12 existing estimation equations. The use of representative samples and external verification is required for more sophisticated estimates in the future.

The Effect of the Mechanical Properties of the 3D Printed Gelatin/Hyaluronic Acid Scaffolds on hMSCs Differentiation Towards Chondrogenesis.

BACKGROUND: Tissue engineering, including 3D bioprinting, holds great promise as a therapeutic tool for repairing cartilage defects. Mesenchymal stem cells have the potential to treat various fields due to their ability to differentiate into different cell types. The biomimetic substrate, such as scaffolds and hydrogels, is a crucial factor that affects cell behavior, and the mechanical properties of the substrate have been shown to impact differentiation during incubation. In this study, we examine the effect of the mechanical properties of the 3D printed scaffolds, made using different concentrations of cross-linker, on hMSCs differentiation towards chondrogenesis. METHODS: The 3D scaffold was fabricated using 3D bioprinting technology with gelatin/hyaluronic acid (HyA) biomaterial ink. Crosslinking was achieved by using different concentrations of 4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methlymorpholinium chloride n-hydrate (DMTMM), allowing for control of the scaffold's mechanical properties. The printability and stability were also evaluated based on the concentration of DMTMM used. The effects of the gelatin/HyA scaffold on chondrogenic differentiation was analyzed by utilizing various concentrations of DMTMM. RESULTS: The addition of HyA was found to improve the printability and stability of 3D printed gelatin/HyA scaffolds. The mechanical properties of the 3D gelatin/HyA scaffold could be regulated through the use of different concentrations of DMTMM cross-linker. In particular, the use of 0.25 mM DMTMM for crosslinking the 3D gelatin/HyA scaffold resulted in enhanced chondrocyte differentiation. CONCLUSION: The mechanical properties of 3D printed gelatin/HyA scaffolds cross-linked using various concentrations of DMTMM can influence the differentiation of hMSCs into chondrocytes.

Biocompatible memristive device based on an agarose@gold nanoparticle-nanocomposite layer obtained from nature for neuromorphic computing.

Natural, organic, materials-based artificial synaptic devices have been in the spotlight for wearable/flexible devices due to their lightweight, biocompatibility, and scalability. In this study, an electronic memristive device based on agarose extracted from plants in the Rhodophyceae class was fabricated, and its memory characteristics and analog data processing capabilities were evaluated. The Al/agarose@gold nanoparticle (AuNP) film/indium-tin-oxide (ITO)-structured memristive device exhibited reliable resistive switching characteristics with excellent retention with a large Ron/Roff ratio of 104. Also, analog conductance changes in our device were achieved with power consumption at the pJ level. This notable behavior could be maintained under mechanical deformations from a flat to a 4-mm bent state. In the recognition simulation based on the device's performance, an 91% accuracy and clear digit classification were achieved.