Electromagnetic Interference Shielding and Joule Heating Properties of Flexible, Lightweight, and Hydrophobic MXene/Nickel-Coated Polyester Fabrics Manufactured by Dip-Dry Coating and Electroless Plating.

High-performance electromagnetic interference (EMI) shielding materials with high flexibility, low density, and hydrophobic surface are crucial for modern integrated electronics and telecommunication systems in advanced industries like aerospace, military, artificial intelligence, and wearable electronics. In this study, we present flexible and hydrophobic MXene/Ni-coated polyester (PET) fabrics featuring a double-layered structure, fabricated via a facile and scalable dip-dry coating process followed by electroless nickel plating. Increasing the dip-dry coating iterations up to 10 cycles boosts the MXene loading content (∼31 wt %) and electrical conductivity (∼86 S/cm) of MXene-coated PET fabrics, while maintaining constant porosity (∼95%). The addition of a Ni layer enhances hydrophobicity, achieving a high water contact angle of ∼114° compared to only MXene-coated PET fabrics (∼49°). Furthermore, the 30 µm thick MXene/Ni-coated PET fabric demonstrates superior electrical conductivity (∼113.8 S/cm) and EMI shielding effectiveness (∼35.7 dB at 8-12 GHz) compared to only MXene- or Ni-coated PET fabrics. The EMI shielding performance of the MXene/Ni-coated PET fabric remains more stable in an air environment than only MXene-coated fabrics due to the outer Ni layer with excellent hydrophobicity and oxidation stability. Additionally, the MXene/Ni-coated PET fabric exhibits impressive Joule heating performance, swiftly converting electrical energy into heat and reaching high steady-state temperatures (32-92 °C) at low applied voltages (0.5-1.5 V).

Fabrication of a tunable photothermal actuator via in situ oxidative polymerization of polydopamine nanoparticles in hydrogel bilayers.

Photothermally triggered actuation enables the remote and local control of a material. The complex actuation can be achieved by controlling the photothermal efficiency of the material, which is crucial for the development of soft actuators. In this study, the photothermal efficiency of a hydrogel bilayer actuator consisting of a passive agarose/alginate double-network hydrogel layer and an active poly(N-isopropylacrylamide) (PNIPAm) layer was controlled via in situ oxidative polymerization of polydopamine nanoparticles (PDA NPs). Highly concentrated PDA NPs were successfully incorporated into the hydrogel bilayer without interrupting or weakening the polymer network during polymerization. The photothermal efficiency of the actuator was controlled using the number of polymerization cycles. Upon light irradiation, the heat generated by the photothermal effect of PDA NPs caused the shrinkage of the PNIPAm layer, resulting in the shape-morphing of the bilayer. The broad light absorption properties of PDA NPs allowed the bilayer to actuate under sunlight or visible light. Finally, we demonstrated controlled photothermal actuation using a pinwheel-shaped actuator consisting of four panels with different photothermal efficiencies.

ANNO: A General Annotation Tool for Bilingual Clinical Note Information Extraction.

OBJECTIVE: This study was conducted to develop a generalizable annotation tool for bilingual complex clinical text annotation, which led to the design and development of a clinical text annotation tool, ANNO. METHODS: We designed ANNO to enable human annotators to support the annotation of information in clinical documents efficiently and accurately. First, annotations for different classes (word or phrase types) can be tagged according to the type of word using the dictionary function. In addition, it is possible to evaluate and reconcile differences by comparing annotation results between human annotators. Moreover, if the regular expression set for each class is updated during annotation, it is automatically reflected in the new document. The regular expression set created by human annotators is designed such that a word tagged once is automatically labeled in new documents. RESULTS: Because ANNO is a Docker-based web application, users can use it freely without being subjected to dependency issues. Human annotators can share their annotation markups as regular expression sets with a dictionary structure, and they can cross-check their annotated corpora with each other. The dictionary-based regular expression sharing function, cross-check function for each annotator, and standardized input (Microsoft Excel) and output (extensible markup language [XML]) formats are the main features of ANNO. CONCLUSIONS: With the growing need for massively annotated clinical data to support the development of machine learning models, we expect ANNO to be helpful to many researchers.

Transparent and Flexible SiOC Films on Colorless Polyimide Substrate for Flexible Cover Window.

We fabricated transparent and flexible silicon oxycarbide (SiOC) hard coating (HC) films on a colorless polyimide substrate to use as cover window films for flexible and foldable displays using a reactive roll-to-roll (R2R) sputtering system at room temperature. At a SiOC thickness of 100 nm, the R2R-sputtered SiOC film showed a high optical transmittance of 87.43% at a visible range of 400 to 800 nm. The R2R-sputtered SiOC films also demonstrated outstanding flexibility, which is a key requirement of foldable and flexible displays. There were no cracks or surface defects on the SiOC films, even after bending (static folding), folding (dynamic folding), twisting, and rolling tests. Furthermore, the R2R-sputtered SiOC film showed good scratch resistance in a pencil hardness test (550 g) and steel wool test under a load of 250 g. To test the impact protection ability, we compared the performance of thin-film heaters (TFHs) and oxide-semiconductor-based thin-film transistors (TFTs) with and without SiOC cover films. The similar performance of the TFHs and TFTs with the SiOC cover window films demonstrate that the R2R-sputtered SiOC films offer promising cover window films for the next generation of flexible or foldable displays.

Transparent Molecular Adhesive Enabling Mechanically Stable ITO Thin Films.

With rapid advances in flexible electronics, transparent conductive electrodes (TCEs) have also been significantly developed as alternatives to the conventional indium tin oxide (ITO)-based material systems that exhibit low mechanical flexibility. Nanomaterial-based alternating materials, such as graphene, nanowire, and nanomesh, exhibit remarkable properties for TCE-based applications, such as high electrical conductivity, high optical transparency, and high mechanical stability. However, these nanomaterial-based systems lack scalability, which is a key requirement for practical applications, and exhibit a size-dependent property variation and inhomogeneous surface uniformity that limit reliable properties over a large area. Here, we exploited a conventional ITO-based material platform; however, we incorporated a transparent molecular adhesive, 4-aminopyridine (4-AP), to improve mechanical flexibility. While the presence of 4-AP barely affected optical transmittance and sheet resistance, it improved interfacial adhesion between the substrate and ITO as well as formed a wavy surface, which could improve the mechanical flexibility. Under various mechanical tests, ITO/4-AP/poly(ethylene terephthalate) (PET) exhibited remarkably improved mechanical flexibility as compared with that of ITO/PET. Furthermore, ITO/4-AP/PET was utilized for a flexible Joule heater application having spatial uniformity of heat generation, voltage-dependent temperature control, and mechanical flexibility under repeated bending tests. This molecular adhesive could overcome the current limitations of material systems for flexible electronics.

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression.

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG's asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG's asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.

A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea.

BACKGROUND: Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. METHODS: We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center's Colorectal Cancer Treatment Protocol (GCCTP). RESULTS: For the C3R model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy. CONCLUSIONS: This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained.

Highly transparent and flexible Ag nanowire-embedded silk fibroin electrodes for biocompatible flexible and transparent heater.

We investigated the electrical, optical and mechanical properties of silver (Ag) nanowire (NW) embedded into a silk fibroin (SF) substrate to create high performance, flexible, transparent, biocompatible, and biodegradable heaters for use in wearable electronics. The Ag NW-embedded SF showed a low sheet resistance of 15 Ω sq-1, high optical transmittance of 85.1%, and a small inner/outer critical bending radius of 1 mm. In addition, the Ag NW-embedded SF showed a constant resistance change during repeated bending, folding, and rolling because the connectivity of the Ag NW embedded into the SF substrate was well maintained. Furthermore, the biocompatible and biodegradable Ag NW-embedded SF substrate served as a flexible interconnector for wearable electronics. The high performance of the transparent and flexible heater demonstrated that an Ag NW-embedded SF-based heater can act as a biocompatible and biodegradable substrate for wearable heaters for the human body.