Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram.

Cosmetics and topical medications, such as gels, foams, creams, and lotions, are viscoelastic substances that are applied to the skin or mucous membranes. The human perception of these materials is complex and involves multiple sensory modalities. Traditional panel-based sensory evaluations have limitations due to individual differences in sensory receptors and factors such as age, race, and gender. Therefore, this study proposes a deep-learning-based method for systematically analyzing and effectively identifying the physical properties of cosmetic gels. Time-series friction signals generated by rubbing the gels were measured. These signals were preprocessed through short-time Fourier transform (STFT) and continuous wavelet transform (CWT), respectively, and the frequency factors that change over time were distinguished and analyzed. The deep learning model employed a ResNet-based convolution neural network (CNN) structure with optimization achieved through a learning rate scheduler. The optimized STFT-based 2D CNN model outperforms the CWT-based 2D and 1D CNN models. The optimized STFT-based 2D CNN model also demonstrated robustness and reliability through k-fold cross-validation. This study suggests the potential for an innovative approach to replace traditional expert panel evaluations and objectively assess the user experience of cosmetics.

Fabrication of a Fully Printed Ammonia Gas Sensor Based on ZnO/rGO Using Ultraviolet-Ozone Treatment.

In this study, a room-temperature ammonia gas sensor using a ZnO and reduced graphene oxide (rGO) composite is developed. The sensor fabrication involved the innovative application of reverse offset and electrostatic spray deposition (ESD) techniques to create a ZnO/rGO sensing platform. The structural and chemical characteristics of the resulting material were comprehensively analyzed using XRD, FT-IR, FESEM, EDS, and XPS, and rGO reduction was achieved via UV-ozone treatment. Electrical properties were assessed through I-V curves, demonstrating enhanced conductivity due to UV-ozone treatment and improved charge mobility from the formation of a ZnO-rGO heterojunction. Exposure to ammonia gas resulted in increased sensor responsiveness, with longer UV-ozone treatment durations yielding superior sensitivity. Furthermore, response and recovery times were measured, with the 10 min UV-ozone-treated sensor displaying optimal responsiveness. Performance evaluation revealed linear responsiveness to ammonia concentration with a high R2 value. The sensor also exhibited exceptional selectivity for ammonia compared to acetone and CO gases, making it a promising candidate for ammonia gas detection. This study shows the outstanding performance and potential applications of the ZnO/rGO-based ammonia gas sensor, promising significant contributions to the field of gas detection.

Temporal Transcriptome Profiling of Pinus densiflora Infected with Pine Wood Nematode Reveals Genetically Programmed Changes upon Pine Wilt Disease.

Pine, an evergreen conifer, is widely distributed worldwide. It is economically, scientifically, and ecologically important. However, pine wilt disease (PWD) induced by the pine wood nematode (PWN) adversely affects pine trees. Many studies have been conducted on the PWN and its beetle vectors to prevent the spread of PWD. However, studies providing a comprehensive understanding of the pine tree transcriptome in response to PWN infection are lacking. Here, we performed temporal profiling of the pine tree transcriptome using PWD-infected red pine trees, Pinus densiflora, inoculated with the PWN by RNA sequencing. Our analysis revealed that defense-responsive genes involved in cell wall modification, jasmonic acid signaling, and phenylpropanoid-related processes were significantly enriched 2 weeks after PWD infection. Furthermore, some WRKY-type and MYB-type transcription factors were upregulated 2 weeks after PWD infection, suggesting that these transcription factors might be responsible for the genome-wide reprogramming of defense-responsive genes in the early PWD stage. Our comprehensive transcriptome analysis will assist in developing PWD-resistant pine trees and identifying genes to diagnose PWD at the early stage of infection, during which large-scale phenotypic changes are absent in PWD-infected pine trees.

Fully Printed Cellulose Nanofiber-Ag Nanoparticle Composite for High-Performance Humidity Sensor.

This paper reports a high-performance humidity sensor made using a novel cellulose nanofiber (CNF)-silver nanoparticle (AgNP) sensing material. The interdigital electrode pattern was printed via reverse-offset printing using Ag nano-ink, and the sensing layer on the printed interdigitated electrode (IDE) was formed by depositing the CNF-AgNP composite via inkjet printing. The structure and morphology of the CNF-AgNP layer are characterized using ultraviolet-visible spectroscopy, an X-ray diffractometer, field emission scanning electron microscopy, energy-dispersive X-ray analysis, and transmission electron microscopy. The humidity-sensing performance of the prepared sensors is evaluated by measuring the impedance changes under the relative humidity variation between 10 and 90% relative humidity. The CNF-AgNP sensor exhibited very sensitive and fast humidity-sensing responses compared to the CNF sensor. The electrode distance effect and the response and recovery times are investigated. The enhanced humidity-sensing performance is reflected in the increased conductivity of the Ag nanoparticles and the adsorption of free water molecules associated with the porous characteristics of the CNF layer. The CNF-AgNP composite enables the development of highly sensitive, fast-responding, reproducible, flexible, and inexpensive humidity sensors.

Integrated Temperature-Humidity Sensors for a Pouch-Type Battery Using 100% Printing Process.

The performance, stability, and lifespan of lithium-ion batteries are influenced by variations in the flow of lithium ions with temperature. In electric vehicles, coolants are generally used to maintain the optimal temperature of the battery, leading to an increasing demand for temperature and humidity sensors that can prevent leakage and short circuits. In this study, humidity and temperature sensors were fabricated on a pouch film of a pouch-type battery. IDE electrodes were screen-printed on the pouch film and humidity- and temperature-sensing materials were printed using a dispenser process. Changes in the capacitance of the printed Ag-CNF film were used for humidity sensing, while changes in the resistance of the printed PEDOT:PSS film were used for temperature sensing. The two sensors were integrated into a single electrode for performance evaluation. The integrated sensor exhibited a response of ΔR ≈ 0.14 to temperature variations from 20 °C to 100 °C with 20% RH humidity as a reference, and a response of ΔC ≈ 2.8 to relative humidity changes from 20% RH to 80% RH at 20 °C. The fabricated integrated sensor is expected to contribute to efficient temperature and humidity monitoring applications in various pouch-type lithium-ion batteries.