Alleviation of neurotoxicity induced by polystyrene nanoplastics by increased exocytosis from neurons.

Nanoplastics (NPs) are potentially toxic and pose a health risk as they can induce an inflammatory response and oxidative stress at cellular and organismal levels. Humans can be exposed to NPs through various routes, including ingestion, inhalation, and skin contact. Notably, uptake into the body via inhalation could result in brain accumulation, which may occur directly across the blood-brain barrier or via other routes. NPs that accumulate in the brain may be endocytosed into neurons, inducing neurotoxicity. Recently, we demonstrated that exposure to polystyrene (PS)-NPs reduces the viability of neurons. We have also reported that inhibiting the retrograde transport of PS-NPs by histone deacetylase 6 (HDAC6) prevents their intracellular accumulation and promotes their export in mouse embryonic fibroblasts. However, whether HDAC6 inhibition can improve neuronal viability by increasing exocytosis of PS-NPs from neurons remains unknown. In this study, mice were intranasally administered fluorescent PS-NPs (PS-YG), which accumulated in the brain and showed potential neurotoxic effects. In cultured neurons, the HDAC6 inhibitor ACY-1215 reduced the fluorescence signal detected from PS-YG, suggesting that the removal of PS-YG from neurons was promoted. Therefore, these results suggest that blocking the retrograde transport of PS-NPs using an HDAC6 inhibitor can alleviate the neurotoxic effects of PS-NPs that enter the brain.

Synthesis of Vapochromic Dyes Having Sensing Properties for Vapor Phase of Organic Solvents Used in Semiconductor Manufacturing Processes and Their Application to Textile-Based Sensors.

Two vapochromic dyes (DMx and DM) were synthesized to be used for textile-based sensors detecting the vapor phase of organic solvents. They were designed to show sensitive color change properties at a low concentration of vapors at room temperature. They were applied to cotton fabrics as a substrate of the textile-based sensors to examine their sensing properties for nine organic solvents frequently used in semiconductor manufacturing processes, such as trichloroethylene, dimethylacetamide, iso-propanol, methanol, n-hexane, ethylacetate, benzene, acetone, and hexamethyldisilazane. The textile sensor exhibited strong sensing properties of polar solvents rather than non-polar solvents. In particular, the detection of dimethylacetamide was the best, showing a color difference of 15.9 for DMx and 26.2 for DM under 300 ppm exposure. Even at the low concentration of 10 ppm of dimethylacetamide, the color change values reached 7.7 and 13.6, respectively, in an hour. The maximum absorption wavelength of the textile sensor was shifted from 580 nm to 550 nm for DMx and 550 nm to 540 nm for DM, respectively, due to dimethylacetamide exposure. The sensing mechanism was considered to depend on solvatochromism, the aggregational properties of the dyes and the adsorption amounts of the solvent vapors on the textile substrates to which the dyes were applied. Finally, the reusability of the textile sensor was tested for 10 cycles.

Molecularly Controlled Stark Effect Induces Significant Rectification in Polycyclic-Aromatic-Hydrocarbon-Terminated n-Alkanethiolates.

The variation of the electronic structure of individual molecules as a function of the applied bias matters for the performance of molecular and organic electronic devices. Understanding the structure-electric-field relationship, however, remains a challenge because of the lack of in-operando spectroscopic technique and complexity arising from the ill-defined on-surface structure of molecules and organic-electrode interfaces within devices. We report that a reliable and reproducible molecular diode can be achieved by control of the conjugation length in polycyclic-aromatic-hydrocarbon (PAH)-terminated n-alkanethiolate (denoted as SC11PAH), incorporated into liquid-metal-based large-area tunnel junctions in the form of a self-assembled monolayer. By taking advantage of the structural simplicity and tunability of SC11PAH and the high-yielding feature of the junction technique, we demonstrate that the increase in the conjugation length of the PAH terminal group leads to a significant rectification ratio up to ∼1.7 × 102 at ±740 mV. Further study suggests that the Stark shift of the molecular energy resonance of the PAH breaks the symmetry of the energy topography across the junction and induces rectification in a temperature-independent charge-transport regime.

Vertical and In-Plane Current Devices Using NbS2/n-MoS2 van der Waals Schottky Junction and Graphene Contact.

A van der Waals (vdW) Schottky junction between two-dimensional (2D) transition metal dichalcogenides (TMDs) is introduced here for both vertical and in-plane current devices: Schottky diodes and metal semiconductor field-effect transistors (MESFETs). The Schottky barrier between conducting NbS2 and semiconducting n-MoS2 appeared to be as large as ∼0.5 eV due to their work-function difference. While the Schottky diode shows an ideality factor of 1.8-4.0 with an on-to-off current ratio of 103-105, Schottky-effect MESFET displays little gate hysteresis and an ideal subthreshold swing of 60-80 mV/dec due to low-density traps at the vdW interface. All MESFETs operate with a low threshold gate voltage of -0.5 ∼ -1 V, exhibiting easy saturation. It was also found that the device mobility is significantly dependent on the condition of source/drain (S/D) contact for n-channel MoS2. The highest room temperature mobility in MESFET reaches to approximately more than 800 cm2/V s with graphene S/D contact. The NbS2/n-MoS2 MESFET with graphene was successfully integrated into an organic piezoelectric touch sensor circuit with green OLED indicator, exploiting its predictable small threshold voltage, while NbS2/n-MoS2 Schottky diodes with graphene were applied to extract doping concentrations in MoS2 channel.

Intrinsic Correlation between Electronic Structure and Degradation: From Few-Layer to Bulk Black Phosphorus.

Black phosphorus (BP) has received much attention owing to its fascinating properties, such as a high carrier mobility and tunable band gap. However, these advantages have been overshadowed by the fast degradation of BP under ambient conditions. To overcome this obstacle, the exact degradation mechanisms need to be unveiled. Herein, we analyzed two sequential degradation processes and the layer-dependent degradation rates of BP in the dark by scanning Kelvin probe microscopy (SKPM) measurements and theoretical modeling. The layer-dependent degradation was successfully interpreted by considering the oxidation model based on the Marcus-Gerischer theory (MGT). In the dark, the electron transfer rate from BP to oxygen molecules depends on the number of layers as these systems have different carrier concentrations. This work not only provides a deeper understanding of the degradation mechanism itself but also suggest new strategies for the design of stable BP-based electronics.

Charge transport and rectification in molecular junctions formed with carbon-based electrodes.

Molecular junctions formed using the scanning-tunneling-microscope-based break-junction technique (STM-BJ) have provided unique insight into charge transport at the nanoscale. In most prior work, the same metal, typically Au, Pt, or Ag, is used for both tip and substrate. For such noble metal electrodes, the density of electronic states is approximately constant within a narrow energy window relevant to charge transport. Here, we form molecular junctions using the STM-BJ technique, with an Au metal tip and a microfabricated graphite substrate, and measure the conductance of a series of graphite/amine-terminated oligophenyl/Au molecular junctions. The remarkable mechanical strength of graphite and the single-crystal properties of our substrates allow measurements over few thousand junctions without any change in the surface properties. We show that conductance decays exponentially with molecular backbone length with a decay constant that is essentially the same as that for measurements with two Au electrodes. More importantly, despite the inherent symmetry of the oligophenylamines, we observe rectification in these junctions. State-of-art ab initio conductance calculations are in good agreement with experiment, and explain the rectification. We show that the highly energy-dependent graphite density of states contributes variations in transmission that, when coupled with an asymmetric voltage drop across the junction, leads to the observed rectification. Together, our measurements and calculations show how functionality may emerge from hybrid molecular-scale devices purposefully designed with different electrodes beyond the so-called "wide band limit," opening up the possibility of assembling molecular junctions with dissimilar electrodes using layered 2D materials.

Determination of energy level alignment and coupling strength in 4,4'-bipyridine single-molecule junctions.

We measure conductance and thermopower of single Au-4,4'-bipyridine-Au junctions in distinct low and high conductance binding geometries accessed by modulating the electrode separation. We use these data to determine the electronic energy level alignment and coupling strength for these junctions, which are known to conduct through the lowest unoccupied molecular orbital (LUMO). Contrary to intuition, we find that, in the high-conductance junction, the LUMO resonance energy is further away from the Au Fermi energy than in the low-conductance junction. However, the LUMO of the high-conducting junction is better coupled to the electrode. These results are in good quantitative agreement with self-energy corrected zero-bias density functional theory calculations. Our calculations show further that measurements of conductance and thermopower in amine-terminated oligophenyl-Au junctions, where conduction occurs through the highest occupied molecular orbitals, cannot be used to extract electronic parameters as their transmission functions do not follow a simple Lorentzian form.

Conductance of molecular junctions formed with silver electrodes.

We compare the conductance of a series of amine-terminated oligophenyl and alkane molecular junctions formed with Ag and Au electrodes using the scanning tunneling microscope based break-junction technique. For these molecules that conduct through the highest occupied molecular orbital, junctions formed with Au electrodes are more conductive than those formed with Ag electrodes, consistent with the lower work function for Ag. The measured conductance decays exponentially with molecular backbone length with a decay constant that is essentially the same for Ag and Au electrodes. However, the formation and evolution of molecular junctions upon elongation are very different for these two metals. Specifically, junctions formed with Ag electrodes sustain significantly longer elongation when compared with Au due to a difference in the initial gap opened up when the metal point-contact is broken. Using this observation and density functional theory calculations of junction structure and conductance we explain the trends observed in the single molecule junction conductance. Our work thus opens a new path to the conductance measurements of a single molecule junction in Ag electrodes.

Multilayered nano-prism vertex tips for tip-enhanced Raman spectroscopy and imaging.

We presented a scalable fabrication method for the preparation of multilayered nano-prism vertex (NV)-tips whose dimensions can be controlled for tip-enhanced Raman spectroscopy (TERS). The NV-tip had sharp vertices (diameter ~20 nm) originated from the chemical lift-off process after the angle-grinding process, enabling high resolution imaging. TERS measurements were performed on brilliant cresyl blue (BCB) molecules using a Ag/Au NV-tip, revealing the enhanced field localization at the vertices of the NV-tip. Furthermore, we could observe the polarization effect of the NV-tip. Our NV-tips should be a powerful tool for basic research on TERS experiments and SPM applications.

Graphene nanonet for biological sensing applications.

We report a simple but efficient method to fabricate versatile graphene nanonet (GNN)-devices. In this method, networks of V2O5 nanowires (NWs) were prepared in specific regions of single-layer graphene, and the graphene layer was selectively etched via a reactive ion etching method using the V2O5 NWs as a shadow mask. The process allowed us to prepare large scale patterns of GNN structures which were comprised of continuous networks of graphene nanoribbons (GNRs) with chemical functional groups on their edges. The GNN can be easily functionalized with biomolecules for fluorescent biochip applications. Furthermore, electrical channels based on GNN exhibited a rather high mobility and low noise compared with other network structures based on nanostructures such as carbon nanotubes, which was attributed to the continuous connection of nanoribbons in GNN structures. As a proof of concept, we built DNA sensors based on GNN channels and demonstrated the selective detection of DNA. Since our method allows us to prepare high-performance networks of GNRs over a large surface area, it should open up various practical biosensing applications.

Nanotube-bridged wires with sub-10 nm gaps.

We report a simple but efficient method to synthesize carbon nanotube-bridged wires (NBWs) with gaps as small as 5 nm. In this method, we have combined a strategy for assembling carbon nanotubes (CNTs) inside anodized aluminum oxide pores and the on-wire lithography technique to fabricate CNT-bridged wires with gap sizes deliberately tailored over the 5-600 nm range. As a proof-of-concept demonstration of the utility of this architecture, we have prepared NBW-based chemical and biosensors which exhibit higher analyte sensitivity (lower limits of detection) than those based on planar CNT networks. This observation is attributed to a greater surface-to-volume ratio of CNTs in the NBWs than those in the planar CNT devices. Because of the ease of synthesis and high yield of NBWs, this technique may enable the further incorporation of CNT-based architectures into various nanoelectronic and sensor platforms.

Dielectric Constant in Nanoscale Bubbles on MoS2.

Nanoscale bubbles form inevitably during the transfer of two-dimensional (2D) materials on a target substrate due to their van der Waals interaction. Despite a large number of studies based on nanobubble structures with localized strain, the dielectric constant (κ) in nanobubbles of MoS2 is poorly understood. Here, we report κ measurements for nanobubbles on MoS2 by probing the polarization forces based on electrostatic force microscopy. Remarkably, higher κ values of 6-8 independent of the nanobubble size are observed for the nanobubbles as compared to flat regions with a κ of ≈3. We find that the charge carrier increase owing to the strain-induced bandgap reduction is responsible for the enhanced κ of the nanobubbles, where the measured κ is in good agreement with the calculations based on the Clausius-Mossotti relation. Our results provide fundamental information about the strain-induced local dielectric properties of 2D materials and a guide for the design and fabrication of high-performance optoelectrical devices based on 2D materials.

Deep-learning-based gestational sac detection in ultrasound images using modified YOLOv7-E6E model.

As the population and income levels rise, meat consumption steadily increases annually. However, the number of farms and farmers producing meat decrease during the same period, reducing meat sufficiency. Information and Communications Technology (ICT) has begun to be applied to reduce labor and production costs of livestock farms and improve productivity. This technology can be used for rapid pregnancy diagnosis of sows; the location and size of the gestation sacs of sows are directly related to the productivity of the farm. In this study, a system proposes to determine the number of gestation sacs of sows from ultrasound images. The system used the YOLOv7-E6E model, changing the activation function from sigmoid-weighted linear unit (SiLU) to a multi-activation function (SiLU + Mish). Also, the upsampling method was modified from nearest to bicubic to improve performance. The model trained with the original model using the original data achieved mean average precision of 86.3%. When the proposed multi-activation function, upsampling, and AutoAugment were applied, the performance improved by 0.3%, 0.9%, and 0.9%, respectively. When all three proposed methods were simultaneously applied, a significant performance improvement of 3.5% to 89.8% was achieved.

Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence.

One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be misdiagnosed as a normal lesion by both primary care physicians and orthopedic surgeons; expert consultation is needed for accurate diagnosis. To overcome this limitation, we developed a transfer learning-based ensemble model to detect and localize fractures. A total of 1050 radiographs, including 100 incomplete fractures, were preprocessed by applying a Sobel filter. Six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) were selected for transfer learning. We then composed two ensemble models; the first was based on the three models having the highest accuracy, and the second was based on the five models having the highest accuracy. The area under the curve (AUC) of the case that used the three most accurate models was the highest at 0.998. This study demonstrates that an ensemble of transfer-learning-based models can accurately classify and detect fractures, even in an imbalanced dataset. This artificial intelligence (AI)-assisted diagnostic application could support decision-making and reduce the workload of clinicians with its high speed and accuracy.

Transfer learning-based ensemble convolutional neural network for accelerated diagnosis of foot fractures.

The complex shape of the foot, consisting of 26 bones, variable ligaments, tendons, and muscles leads to misdiagnosis of foot fractures. Despite the introduction of artificial intelligence (AI) to diagnose fractures, the accuracy of foot fracture diagnosis is lower than that of conventional methods. We developed an AI assistant system that assists with consistent diagnosis and helps interns or non-experts improve their diagnosis of foot fractures, and compared the effectiveness of the AI assistance on various groups with different proficiency. Contrast-limited adaptive histogram equalization was used to improve the visibility of original radiographs and data augmentation was applied to prevent overfitting. Preprocessed radiographs were fed to an ensemble model of a transfer learning-based convolutional neural network (CNN) that was developed for foot fracture detection with three models: InceptionResNetV2, MobilenetV1, and ResNet152V2. After training the model, score class activation mapping was applied to visualize the fracture based on the model prediction. The prediction result was evaluated by the receiver operating characteristic (ROC) curve and its area under the curve (AUC), and the F1-Score. Regarding the test set, the ensemble model exhibited better classification ability (F1-Score: 0.837, AUC: 0.95, Accuracy: 86.1%) than other single models that showed an accuracy of 82.4%. With AI assistance for the orthopedic fellow, resident, intern, and student group, the accuracy of each group improved by 3.75%, 7.25%, 6.25%, and 7% respectively and diagnosis time was reduced by 21.9%, 14.7%, 24.4%, and 34.6% respectively.

An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms.

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

Floating electrode transistor based on purified semiconducting carbon nanotubes for high source-drain voltage operation.

We report floating-electrode-based thin-film transistors (F-TFTs) based on a purified semiconducting single-walled carbon nanotube (swCNT) network for a high source-drain voltage operation. At a high source-drain voltage, a conventional swCNT-TFT exhibited poor transistor performance with a small on-off ratio, which was attributed to the reduced Schottky barrier modulation at a large bias. In the F-TFT device, an swCNT network channel was separated into a number of channels connected by floating electrodes. The F-TFTs exhibited a much higher on-off ratio than a conventional swCNT-TFT with a single channel. This work should provide an important guideline in designing swCNT-TFTs for high voltage applications such as displays.

Nondestructive and local mapping photoresponse of WSe2 by electrostatic force microscopy.

We report a local mapping photoresponse of WSe2 using a second-harmonic (2w) channel based on nondestructive electrostatic force microscopy (EFM). The 2w signals resulting from interaction between WSe2 and EFM tip are intrinsically related to the electrical conductivity of WSe2. The photoresponse images and rise/decay time constants of WSe2 are obtained by local mapping 2w signals under illumination. We observe that the local photoresponse signals of WSe2 increase with the positive tip gate voltage while the WSe2 shows a p-type behavior in dark conditions We find that the reduced mobility of the photogenerated charge carriers resulting from the enhanced carrier scattering in the accumulation regime of WSe2 is responsible for the gate-dependent photoresponse behavior. Our results provide a deep understanding the intrinsic optoelectrical properties of WSe2 and contribute to the developments in the optoelectronic devices based on van der Waals layered materials.

Observing the Layer-Number-Dependent Local Dielectric Response of WSe2 by Electrostatic Force Microscopy.

We investigate the layer-number-dependent dielectric response of WSe2 by measuring the phase shift (Φ) through an electrostatic force microscopy (EFM). The measured Φ results stem mainly from the capacitive coupling between the tip and WSe2 based on the plane capacitor model, leading to changes in the second derivative of the capacitance (C'') values, which increase in a few layers and saturate to the bulk value under an applied EFM tip bias. The C'' value is related to the dielectric polarization, reflecting the charge carrier concentration and mobility of WSe2 flakes with different numbers of layers. This implies that the dielectric constant of WSe2 shows layer-number-dependent behavior which increases with the number of layers, approaching the bulk value. Furthermore, we also construct a spatially resolved C'' map to observe the local dielectric response of WSe2 flakes. Our work could be significant in that it can improve the performance of novel electronic devices based on the controllable dielectric properties of 2D vdW semiconductor materials.

Highly transparent phototransistor based on quantum-dots and ZnO bilayers for optical logic gate operation in visible-light.

Highly transparent optical logic circuits operated with visible light signals are fabricated using phototransistors with a heterostructure comprised of an oxide semiconductor (ZnO) with a wide bandgap and quantum dots (CdSe/ZnS QDs) with a small bandgap. ZnO serves as a highly transparent active channel, while the QDs absorb visible light and generate photoexcited charge carriers. The induced charge carriers can then be injected into the ZnO conduction band from the QD conduction band, which enables current to flow to activate the phototransistor. The photoexcited charge transfer mechanism is investigated using time-resolved photoluminescence spectroscopy, scanning Kelvin probe microscopy, and ultraviolet photoelectron spectroscopy. Measurements show that carriers in the QD conduction band can transfer to the ZnO conduction band under visible light illumination due to a change in the Fermi energy level. Moreover, the barrier for electron injection into the ZnO conduction band from the QD conduction band is low enough to allow photocurrent generation in the QDs/ZnO phototransistor. Highly transparent NOT, NOR, and NAND optical logic circuits are fabricated using the QDs/ZnO heterostructure and transparent indium tin oxide electrodes. This work provides a means of developing highly transparent optical logic circuits that can operate under illumination with low-energy photons such as those found in visible light.