High energy density in artificial heterostructures through relaxation time modulation.

Electrostatic capacitors are foundational components of advanced electronics and high-power electrical systems owing to their ultrafast charging-discharging capability. Ferroelectric materials offer high maximum polarization, but high remnant polarization has hindered their effective deployment in energy storage applications. Previous methodologies have encountered problems because of the deteriorated crystallinity of the ferroelectric materials. We introduce an approach to control the relaxation time using two-dimensional (2D) materials while minimizing energy loss by using 2D/3D/2D heterostructures and preserving the crystallinity of ferroelectric 3D materials. Using this approach, we were able to achieve an energy density of 191.7 joules per cubic centimeter with an efficiency greater than 90%. This precise control over relaxation time holds promise for a wide array of applications and has the potential to accelerate the development of highly efficient energy storage systems.

Compound C inhibits the replication of feline coronavirus.

Feline Coronavirus (FCoV) is a viral pathogen of cats and a highly contagious virus. Cats in a cattery can be infected by up to 100%, and even household cats are infected by 20-60%. Some strains of FCoV are known to induce a fatal disease in cats named Feline Infectious Peritonitis (FIP). However, no effective treatments are available. We demonstrated that compound C (dorsomorphin) can potentially inhibit feline coronavirus replication. Compound C treatment decreased the FCoV-induced plaque formation and cytopathic effect in FCoV-infected cells. Compound C treatment also significantly reduced the amount of viral RNA and viral protein in the cells in a dose-dependent manner. Our findings suggest that compound C is potentially useful for feline coronavirus-related diseases.

Predicting response to anti-EGFR antibody, cetuximab, therapy by monitoring receptor internalization and degradation.

Anti-epidermal growth factor receptor (EGFR) antibody, cetuximab, therapy has significantly improved the clinical outcomes of patients with colorectal cancer, but the response to cetuximab can vary widely among individuals. We thus need strategies for predicting the response to this therapy. However, the current methods are unsatisfactory in their predictive power. Cetuximab can promote the internalization and degradation of EGFR, and its therapeutic efficacy is significantly correlated with the degree of EGFR degradation. Here, we present a new approach to predict the response to anti-EGFR therapy, cetuximab by evaluating the degree of EGFR internalization and degradation of colorectal cancer cells in vitro and in vivo. Our newly developed fluorogenic cetuximab-conjugated probe (Cetux-probe) was confirmed to undergo EGFR binding, internalization, and lysosomal degradation to yield fluorescence activation; it thus shares the action mechanism by which cetuximab exerts its anti-tumor effects. Cetux-probe-activated fluorescence could be used to gauge EGFR degradation and showed a strong linear correlation with the cytotoxicity of cetuximab in colorectal cancer cells and tumor-bearing mice. The predictive ability of Cetux-probe-activated fluorescence was much higher than those of EGFR expression or KRAS mutation status. The Cetux-probes may become useful tools for predicting the response to cetuximab therapy by assessing EGFR degradation.

Controlled spatial characteristics of ligands on nanoparticles: Determinant of cellular functions.

Interactions of various ligands and receptors have been extensively investigated because they regulate a series of signal transduction leading to various functional cellular outcomes. The receptors on cell membrane recognize their specific ligands, resulting in specific binding between ligands and receptors. Accumulating evidence reveals that the receptors recognize the difference on the spatial characteristics of ligands as well as the types of ligands. Thus, control on spatial characteristics of multiple ligands presented on therapeutic nanoparticles is believed to impact the cellular functions. Specifically, the localized and multivalent distribution of ligands on nanoparticles can induce receptor oligomerization and receptor clustering, controlling intensity or direction of signal transduction cascades. Here, we will introduce recent studies on the use of material-based nanotechnology to control spatial characteristics of ligands and their effect on cellular functions. These therapeutic nanoparticles with controlled spatial characteristics of ligands may be a promising strategy for maximized therapeutic outcome.

COVID-19 Cases and Deaths among Healthcare Personnel with the Progression of the Pandemic in Korea from March 2020 to February 2022.

Healthcare personnel (HCP) are vulnerable to COVID-19 infection due to their higher risk of contact with infected persons. The numbers of cases and deaths among HCP in Korea were divided into four periods associated with different major variants of SARS-CoV-2: GH clade, Alpha, Delta, and Omicron. To evaluate the implication of HCP infection in Korea, we overviewed the pandemic status in Korea and in other countries: the cases, deaths, excess mortality, and vaccination rates in Germany, Israel, Italy, Japan, the United Kingdom, and the United States. In about two years, there were 10,670 HCP cases among all COVID-19 cases (1.15% of 925,975 cases). HCP cases had a lower death rate (%) compared to that for all cases (0.14 versus 0.75). Nurses were the most infected (55.3%), followed by HCP of other categories (28.8%) and doctors (15.9%), while deaths were mostly reported among doctors (9 out of 15, 60%). Cases among HCP gradually increased, but the death rate decreased as the pandemic progressed. Compared to five of the other countries examined, Korea had a higher incidence of cases but a lower mortality, lower excess mortality, and a higher vaccination rate.

Considering cell volume in dopant screening for improving Li-ion mobility in an amorphous LiPON solid-state electrolyte: an ab initio study.

Engineering of solid electrolytes of Li-ion batteries is carried out for achieving high levels of ionic conductivity and preserving low levels of electrical conductivity. Doping metallic elements into solid electrolyte materials composed of Li, P, and O is quite challenging due to instances of possible decomposition and secondary phase formation. To accelerate the development of high-performance solid electrolytes, predictions of thermodynamic phase stabilities and conductivities are necessary, as they would avoid the need to carry out exhaustive trial-and-error experiments. In this study, we demonstrated theoretical approach to increase the ionic conductivity of amorphous solid electrolyte by doping: cell volume-ionic conductivity relation. Using density functional theory (DFT) calculations, we examined the validity of the hypothetical principle in predicting improvements in stability and ionic conductivity with 6 candidate doping elements (Si, Ti, Sn, Zr, Ce, Ge) in a quaternary Li-P-O-N solid electrolyte system (LiPON) both in crystalline and amorphous phases. The doping of Si into LiPON (Si-LiPON) was indicated to stabilize the system and enhance ionic conductivity based on our calculated doping formation energy and cell volume change. The proposed doping strategies provide crucial guidelines for the development of solid-state electrolytes with enhanced electrochemical performances.

High-throughput manufacturing of epitaxial membranes from a single wafer by 2D materials-based layer transfer process.

Layer transfer techniques have been extensively explored for semiconductor device fabrication as a path to reduce costs and to form heterogeneously integrated devices. These techniques entail isolating epitaxial layers from an expensive donor wafer to form freestanding membranes. However, current layer transfer processes are still low-throughput and too expensive to be commercially suitable. Here we report a high-throughput layer transfer technique that can produce multiple compound semiconductor membranes from a single wafer. We directly grow two-dimensional (2D) materials on III-N and III-V substrates using epitaxy tools, which enables a scheme comprised of multiple alternating layers of 2D materials and epilayers that can be formed by a single growth run. Each epilayer in the multistack structure is then harvested by layer-by-layer mechanical exfoliation, producing multiple freestanding membranes from a single wafer without involving time-consuming processes such as sacrificial layer etching or wafer polishing. Moreover, atomic-precision exfoliation at the 2D interface allows for the recycling of the wafers for subsequent membrane production, with the potential for greatly reducing the manufacturing cost.

Chip-less wireless electronic skins by remote epitaxial freestanding compound semiconductors.

Recent advances in flexible and stretchable electronics have led to a surge of electronic skin (e-skin)-based health monitoring platforms. Conventional wireless e-skins rely on rigid integrated circuit chips that compromise the overall flexibility and consume considerable power. Chip-less wireless e-skins based on inductor-capacitor resonators are limited to mechanical sensors with low sensitivities. We report a chip-less wireless e-skin based on surface acoustic wave sensors made of freestanding ultrathin single-crystalline piezoelectric gallium nitride membranes. Surface acoustic wave-based e-skin offers highly sensitive, low-power, and long-term sensing of strain, ultraviolet light, and ion concentrations in sweat. We demonstrate weeklong monitoring of pulse. These results present routes to inexpensive and versatile low-power, high-sensitivity platforms for wireless health monitoring devices.

Ultraefficient Electrocatalytic Hydrogen Evolution from Strain-Engineered, Multilayer MoS2.

This paper reports an approach to repurpose low-cost, bulk multilayer MoS2 for development of ultraefficient hydrogen evolution reaction (HER) catalysts over large areas (>cm2). We create working electrodes for use in HER by dry transfer of MoS2 nano- and microflakes to gold thin films deposited on prestrained thermoplastic substrates. By relieving the prestrain at a macroscopic scale, a tunable level of tensile strain is developed in the MoS2 and consequently results in a local phase transition as a result of spontaneously formed surface wrinkles. Using electrochemical impedance spectroscopy, we verified that electrochemical activation of the strained MoS2 lowered the charge transfer resistance within the materials system, achieving HER activity comparable to platinum (Pt). Raman and X-ray photoelectron spectroscopy show that desulfurization in the multilayer MoS2 was promoted by the phase transition; the combined effect of desulfurization and the lower charge resistance induced superior HER performance.

TIMP-1-expressing breast tumor spheroids for the evaluation of drug penetration and efficacy.

Abundance of stromal cells and extracellular matrix (ECM) is observed in breast cancer, acting as a barrier for drug penetration and presenting a key issue for developing efficient therapeutics. In this study, we aimed to develop a three-dimensional (3D) multicellular tumor model comprising cancer and stromal cells that could effectively mimic the drug resistance properties of breast cancer. Three different types of spheroid models were designed by co-culturing breast cancer cells (MDA-MB-231) with three different types of stromal cells: human adipose-derived stromal cells (hASCs), human bone marrow stromal cells, or human dermal fibroblasts. Compared with other models, in the hASC co-culture model, tissue inhibitor of metalloproteinases-1 (TIMP-1) was highly expressed and the activity of matrix metalloproteinases was decreased, resulting in a higher ECM deposition on the spheroid surfaces. This spheroid model showed less drug penetration and treatment efficacy than the other models. TIMP-1 silencing in hASCs reduced ECM protein expression and increased drug penetration and vulnerability. A quantitative structure-activity relationship study using multiple linear regression drew linear relationships between the chemical properties of drugs and experimentally determined permeability values. Drugs that did not match the drug-likeness rules exhibited lower permeability in the 3D tumor model. Taken together, our findings indicate that this 3D multicellular tumor model may be used as a reliable platform for efficiently screening therapeutics agents for solid tumors.

Graphene Buffer Layer on SiC as a Release Layer for High-Quality Freestanding Semiconductor Membranes.

Free-standing crystalline membranes are highly desirable owing to recent developments in heterogeneous integration of dissimilar materials. Van der Waals (vdW) epitaxy enables the release of crystalline membranes from their substrates. However, suppressed nucleation density due to low surface energy has been a challenge for crystallization; reactive materials synthesis environments can induce detrimental damage to vdW surfaces, often leading to failures in membrane release. This work demonstrates a novel platform based on graphitized SiC for fabricating high-quality free-standing membranes. After mechanically removing epitaxial graphene on a graphitized SiC wafer, the quasi-two-dimensional graphene buffer layer (GBL) surface remains intact for epitaxial growth. The reduced vdW gap between the epilayer and substrate enhances epitaxial interaction, promoting remote epitaxy. Significantly improved nucleation and convergent quality of GaN are achieved on the GBL, resulting in the best quality GaN ever grown on two-dimensional materials. The GBL surface exhibits excellent resistance to harsh growth environments, enabling substrate reuse by repeated growth and exfoliation.

Hydrogen production by electrochemical reaction using ethylene glycol with terephthalic acid.

In this study, ethylene glycol (EG) and terephthalic acid (TPA) were used to generate hydrogen using copper electrodes in an alkaline aqueous solution and the corresponding reaction mechanism was experimentally investigated. Both EG and TPA produced hydrogen; however, TPA consumed OH-, inhibiting the production of intermediary compounds of EG and causing EG to actively react with H2O, ultimately leading to enhanced hydrogen production. In addition, the initiation potential of water decomposition of the EG and TPA alkaline aqueous solution was 1.0 V; when 1.8 V (vs. RHE) was applied, the hydrogen production reached 440 mmol L-1, which was substantially greater than the hydrogen production rate of 150 mmol L-1 during water decomposition.

Correction: Hydrogen production by electrochemical reaction using ethylene glycol with terephthalic acid.

[This corrects the article DOI: 10.1039/D0RA10187G.].

Semantic Point Cloud Mapping of LiDAR Based on Probabilistic Uncertainty Modeling for Autonomous Driving.

LiDAR-based Simultaneous Localization And Mapping (SLAM), which provides environmental information for autonomous vehicles by map building, is a major challenge for autonomous driving. In addition, the semantic information has been used for the LiDAR-based SLAM with the advent of deep neural network-based semantic segmentation algorithms. The semantic segmented point clouds provide a much greater range of functionality for autonomous vehicles than geometry alone, which can play an important role in the mapping step. However, due to the uncertainty of the semantic segmentation algorithms, the semantic segmented point clouds have limitations in being directly used for SLAM. In order to solve the limitations, this paper proposes a semantic segmentation-based LiDAR SLAM system considering the uncertainty of the semantic segmentation algorithms. The uncertainty is explicitly modeled by proposed probability models which are come from the data-driven approaches. Based on the probability models, this paper proposes semantic registration which calculates the transformation relationship of consecutive point clouds using semantic information with proposed probability models. Furthermore, the proposed probability models are used to determine the semantic class of the points when the multiple scans indicate different classes due to the uncertainty. The proposed framework is verified and evaluated by the KITTI dataset and outdoor environments. The experiment results show that the proposed semantic mapping framework reduces the errors of the mapping poses and eliminates the ambiguity of the semantic information of the generated semantic map.

Graphene-assisted spontaneous relaxation towards dislocation-free heteroepitaxy.

Although conventional homoepitaxy forms high-quality epitaxial layers1-5, the limited set of material systems for commercially available wafers restricts the range of materials that can be grown homoepitaxially. At the same time, conventional heteroepitaxy of lattice-mismatched systems produces dislocations above a critical strain energy to release the accumulated strain energy as the film thickness increases. The formation of dislocations, which severely degrade electronic/photonic device performances6-8, is fundamentally unavoidable in highly lattice-mismatched epitaxy9-11. Here, we introduce a unique mechanism of relaxing misfit strain in heteroepitaxial films that can enable effective lattice engineering. We have observed that heteroepitaxy on graphene-coated substrates allows for spontaneous relaxation of misfit strain owing to the slippery graphene surface while achieving single-crystalline films by reading the atomic potential from the substrate. This spontaneous relaxation technique could transform the monolithic integration of largely lattice-mismatched systems by covering a wide range of the misfit spectrum to enhance and broaden the functionality of semiconductor devices for advanced electronics and photonics.

Path towards graphene commercialization from lab to market.

The ground-breaking demonstration of the electric field effect in graphene reported more than a decade ago prompted the strong push towards the commercialization of graphene as evidenced by a wealth of graphene research, patents and applications. Graphene flake production capability has reached thousands of tonnes per year, while continuous graphene sheets of tens of metres in length have become available. Various graphene technologies developed in laboratories have now transformed into commercial products, with the very first demonstrations in sports goods, automotive coatings, conductive inks and touch screens, to name a few. Although challenges related to quality control in graphene materials remain to be addressed, the advancement in the understandings of graphene will propel the commercial success of graphene as a compelling technology. This Review discusses the progress towards commercialization of graphene for the past decade and future perspectives.

Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles.

Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either d y n a m i c or s t a t i c depending on their movement characteristics. A LiDAR point cloud is also segmented into d y n a m i c and s t a t i c points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into d y n a m i c and s t a t i c points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR's laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes: d y n a m i c , s t a t i c , and u n k n o w n . Points are placed in the u n k n o w n class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods.

Reverse electrodialysis (RED) using a bipolar membrane to suppress inorganic fouling around the cathode.

When operating reverse electrodialysis (RED) with several hundreds of cell pairs, a large stack voltage of more than 10 V facilitates water electrolysis, even when redox couples are employed for the electrode reaction. Upon feeding natural water containing multivalent ions, ion crossover through a shielding membrane causes inorganic scaling around the cathode and the interior of the membrane stack, due to the combination with the hydroxide ions produced via water reduction. In this work, we introduce a bipolar membrane (BPM) as a shielding membrane at the cathode to suppress inorganic precipitation. Water splitting in the bilayer structure of the BPM can block the ions diffusing from the catholyte and the feed solution, maintaining the current density. To evaluate the effect of the BPM on the inorganic precipitates, diluted sea salt solution is allowed to flow through the outermost feed channel near the cathode, in order to maintain as large a stack voltage as possible, which is important to induce water splitting in the BPM when incorporated into an RED stack of 100 cell pairs. We measure the electric power of the RED according to the arrangement of the BPM and compare it with that of conventional RED. The degree of inorganic scaling is also compared according to the kind of shielding membrane used (anion exchange membrane, cation exchange membrane, and BPM (Neosepta or Fumasep)). The BPM (Neosepta) shows the best performance for suppressing the formation of precipitates. It can hence be used to design a highly stable electrode system for long-term operation of a large-scale RED feeding natural water.

Pattern Learning Electronic Density of States.

Electronic density of states (DOS) is a key factor in condensed matter physics and material science that determines the properties of metals. First-principles density-functional theory (DFT) calculations have typically been used to obtain the DOS despite the considerable computation cost. Herein, we report a fast machine learning method for predicting the DOS patterns of not only bulk structures but also surface structures in multi-component alloy systems by a principal component analysis. Within this framework, we use only four features to define the composition, atomic structure, and surfaces of alloys, which are the d-orbital occupation ratio, coordination number, mixing factor, and the inverse of miller indices. While the DFT method scales as O(N3) in which N is the number of electrons in the system size, our pattern learning method can be independent on the number of electrons. Furthermore, our method provides a pattern similarity of 91 ~ 98% compared to DFT calculations. This reveals that our learning method will be an alternative that can break the trade-off relationship between accuracy and speed that is well known in the field of electronic structure calculations.

Assessing the behavior of the feed-water constituents of a pilot-scale 1000-cell-pair reverse electrodialysis with seawater and municipal wastewater effluent.

Reverse electrodialysis (RED) has vast potential as a clean, nonpolluting, and sustainable renewable energy source; however, pilot-scale RED studies employing real waters remain rare. This study reports the largest RED (1000 cell pairs, 250 m2) with municipal wastewater effluent (1.3-5.7 mS/cm) and seawater (52.9-53.8 mS/cm) as feed solutions. The RED stack was operated at a velocity of 1.5 cm/s and the pilot plant produced 95.8 W of power (0.38 W/m2total membrane or 0.76 W/m2cell pair). During operation of the RED, the inlet design of the stack, comprising thin spacers, and the water dissociation reaction at the cathode were revealed as vulnerabilities of the stack. Specifically, pressure drops at the fluid inlet parts had the most detrimental effects on power output due to clogged spacers around the inlet parts. In addition, precipitates resulting in inorganic fouling were inevitable during the water dissociation reaction due to significant potential generated by the stack in the cathode chamber. Na+ and Cl- accounted for the majority of ions transferred from seawater to wastewater effluent through ion exchange membranes (IEMs). Moreover, some divalent cations in seawater, Mg2+ and Ca2+, were also transferred to the wastewater effluent. Some organics with relatively low molecular weights in the wastewater effluent passed through the IEMs, and their hydrophobic properties elevated the specific UV absorbance (SUVA) level in the seawater.