Approach to Low Contact Resistance Formation on Buried Interface in Oxide Thin-Film Transistors: Utilization of Palladium-Mediated Hydrogen Pathway.

Amorphous oxide semiconductors (AOSs) with low off-currents and processing temperatures offer promising alternative materials for next-generation high-density memory devices. The complex vertical stacking process of memory devices significantly increases the probability of encountering internal contact issues. Conventional surface treatment methods developed for planar devices necessitate efficient approaches to eliminate contact issues at deep internal interfaces in the nanoscale complex structures of AOS devices. In this work, we report the pioneering use of palladium thin film as a high-efficiency active hydrogen transfer pathway from the outside to the internal contact interface via low-temperature postannealing in the H2 atmosphere, and the formation of highly conductive metallic interlayer effectively solves the contact issues at the deeply buried interfaces in devices. The application of this method reduced the contact resistance of Pd electrodes/amorphous indium-gallium-zinc oxide (a-IGZO) thin-film by 2 orders of magnitude, and thereby the mobility of thin-film transistor was increased from 3.2 cm2 V-1 s-1 to nearly 20 cm2 V-1 s-1, preserving an excellent bias stress stability. This technology has wide applicability for the solution of contact resistance issues in oxide semiconductor devices with complex architectures.

Dual-Functional Solar-to-Steam Generation and SERS Detection Substrate Based on Plasmonic Nanostructure.

Solar-to-steam (STS) generation based on plasmonic materials has attracted significant attention as a green method for producing fresh water. Herein, a simple in situ method is introduced to fabricate Au nanoparticles (AuNPs) on cellulose filter papers as dual-functional substrates for STS generation and surface-enhanced Raman spectroscopy (SERS) sensing. The substrates exhibit 90% of broadband solar absorption between 350 and 1800 nm and achieve an evaporation rate of 0.96 kg·m-2·h-1 under 1-sun illumination, room temperature of 20 °C, and relative humidity of 40%. The STS generation of the substrate is stable during 30 h continuous operation. Enriched SERS hotspots between AuNPs endow the substrates with the ability to detect chemical contamination in water with ppb limits of detection for rhodamine 6G dye and melamine. To demonstrate dual-functional properties, the contaminated water was analyzed with SERS and purified by STS. The purified water was then analyzed with SERS to confirm its purity. The developed substrate can be an improved and suitable candidate for fresh water production and qualification.

Surface Modification of ZrO2 Nanoparticles with TEOS to Prepare Transparent ZrO2@SiO2-PDMS Nanocomposite Films with Adjustable Refractive Indices.

Here, highly transparent nanocomposite films with an adjustable refractive index were fabricated through stable dispersion of ZrO2 (n = 2.16) nanoparticles (NPs) subjected to surface modification with SiO2 (n = 1.46) in polydimethylsiloxane (PDMS) (n = 1.42) using the Stöber method. ZrO2 NPs (13.7 nm) were synthesized using conventional hydrothermal synthesis, and their surface modification with SiO2 (ZrO2@SiO2 NPs) was controlled by varying the reaction time (3-54 h). The surface modification of the NPs was characterized using Fourier-transform infrared spectroscopy, dynamic light scattering, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy, and ellipsometry. The surface modification was monitored, and the effective layer thickness of SiO2 varied from 0.1 nm to 4.2 nm. The effective refractive index of the ZrO2@SiO2 NPs at λ = 633 nm was gradually reduced from 2.16 to 1.63. The 100 nm nanocomposite film was prepared by spin-coating the dispersion of ZrO2@SiO2 NPs in PDMS on the coverslip. The nanocomposite film prepared using ZrO2@SiO2 NPs with a reaction time of 18 h (ZrO2@SiO2-18h-PDMS) exhibited excellent optical transparency (Taverage = 91.1%), close to the transparency of the coverslip (Taverage = 91.4%) in the visible range, and an adjustable refractive index (n = 1.42-1.60) as the NP content in the film increased from 0 to 50.0 wt%.

Development of Collagen-Based 3D Matrix for Gastrointestinal Tract-Derived Organoid Culture.

Organoid is a cell organization grown in a three-dimensional (3D) culture system which represents all characteristics of its origin. However, this organ-like structure requires supporting matrix to maintain its characteristics and functions. Matrigel, derived from mouse sarcoma, has often been used as the supporting matrix for organoids, but the result may not be desirable for clinical applications because of the unidentified components from the mouse sarcoma. On the other hand, natural characteristics of collagen emphasize toxic-free friendly niche to both organoid and normal tissue. Hence, this study attempts to develop a new, collagen-based matrix that may substitute Matrigel in organoid culture. Collagen-based matrix was made, using type 1 collagen, Ham's F12 nutrient mixture, and bicarbonate. Then, characteristics of mouse colon organoids were analyzed by morphology and quantitative messenger RNA (mRNA) expression, revealing that the mouse colon organoids grown in the collagen-based matrix and in Matrigel had quite similar morphology, specific markers, and proliferative rates. Mouse small intestine-derived organoids, stomach-derived organoids, and human colon-derived organoids were also cultured, all of which were successfully grown in the collagen-based matrix and had similar properties compared to those cultured in Matrigel. Furthermore, possibility of organoid transplantation was observed. When mouse colon organoids were transplanted with collagen matrix into the EDTA-colitis mouse model, colon organoids were successfully engrafted in damaged tissue. For that reason, the use of collagen-based matrix in organoid culture will render organoid cultivation less expensive and clinically applicable.

Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma.

Lung cancer is the second most common cancer in the United States and the leading cause of mortality in cancer patients. Biomarkers predicting survival of patients with lung cancer have a profound effect on patient prognosis and treatment. However, predictive biomarkers for survival and their relevance for lung cancer are not been well known yet. The objective of this study was to perform machine learning with data from The Cancer Genome Atlas of patients with lung adenocarcinoma (LUAD) to find survival-specific gene mutations that could be used as survival-predicting biomarkers. To identify survival-specific mutations according to various clinical factors, four feature selection methods (information gain, chi-squared test, minimum redundancy maximum relevance, and correlation) were used. Extracted survival-specific mutations of LUAD were applied individually or as a group for Kaplan-Meier survival analysis. Mutations in MMRN2 and GMPPA were significantly associated with patient mortality while those in ZNF560 and SETX were associated with patient survival. Mutations in DNAJC2 and MMRN2 showed significant negative association with overall survival while mutations in ZNF560 showed significant positive association with overall survival. Mutations in MMRN2 showed significant negative association with disease-free survival while mutations in DRD3 and ZNF560 showed positive associated with disease-free survival. Mutations in DRD3, SETX, and ZNF560 showed significant positive association with survival in patients with LUAD while the opposite was true for mutations in DNAJC2, GMPPA, and MMRN2. These gene mutations were also found in other cohorts of LUAD, lung squamous cell carcinoma, and small cell lung cancer. In LUAD of Pan-Lung Cancer cohort, mutations in GMPPA, DNAJC2, and MMRN2 showed significant negative associations with survival of patients while mutations in DRD3 and SETX showed significant positive association with survival. In this study, machine learning was conducted to obtain information necessary to discover specific gene mutations associated with the survival of patients with LUAD. Mutations in the above six genes could predict survival rate and disease-free survival rate in patients with LUAD. Thus, they are important biomarker candidates for prognosis.