arXiv subjects

Chaoyu Zhao

Publications and source records attributed to Chaoyu Zhao.

3 recordsLinked to original sources

arXiv2026

Lingering Sweetness of Ethanol Clusters: Sensory Discovery and Objective Discrimination by Impedance-Based Electronic Tongue

Ethanol is conventionally perceived only as a pungent tastant, while the potential sweet properties of ethanol clusters have remained unrecognized. Here we show that ethanol tetramers exhibit a unique time dependent lingering sweetness, distinct from the immediate upupfront sweetness of conventional sweeteners. To objectively verify this phenomenon, we developed an impedance based bionic electronic tongue that monitors changes at a lipid polymer membrane interface. Sensory evaluation revealed increasing ethanol cluster content from 4.83% to 29.53% prolonged sweetness duration from 10s to 25s, whereas sweetness intensity remained low to moderate (1.0-1.7 on a 10 point scale). Using this electronic tongue, we achieved objective discrimination between upfront and lingering sweetness,full band phase angle scanning identified optimal frequencies of 0.933Hz (t=0.171s) for xylitol (fast binding and dissociation) and 0.03Hz (t=5.305s) for ethanol clusters (slow binding and dissociation). Phase angle responses correlated strongly with sensory sweetness duration. In natural aged Chinese Baijiu (1-20 years), low frequency phase angle signals positively correlated with sensory lingering sweetness scores, confirming that ethanol cluster accumulation during aging underpins the sweetness of Baijiu. This work expands the classic AH-B sweetness theory from single molecules to supramolecular clusters and provides an analytical platform for probing the temporal dimension of flavor perception.

physics.atom-ph↗

arXiv2023

Three-dimensional study of grain scale tensile twinning activity in Mg: A combination of microstructure characterization and mechanical modeling

Tensile twinning is a main deformation mode in hexagonal close packed structure metals, so it is important to comprehensively understand twinning mechanisms which are not fully disclosed using 2D or small volume 3D characterization techniques. A large area 3D electron backscatter diffraction (EBSD) measurement and crystal plasticity modeling were carried out to investigate the tensile twinning behaviors in a Mg alloy. The results showed that tensile twinning activity was underestimated using conventional 2D EBSD scans. When compressed to yield point, the examined twin frequency with 2D was lower than that using 3D EBSD. The effects of Schmid factor (SF) on twinning were investigated. Almost all high Schmid factor (SF>0.35) grains were twinned. A surprising high twin frequency of 82% in middle SF (0.35>=SF>=0.15) grains was observed, which was unexpected since the middle SF grains were believed to be unfavorable for twinning. The twin frequency in low SF (SF<0.15) grains was slightly increased from 2D to 3D EBSD due to the small volume of twins. The shear stress maintained a high level and was homogeneously distributed in high SF grains, facilitating twin nucleation and growth. The shear stress was distributed heterogeneously within the middle SF grains, and twins were nucleated within areas with positive shear stress. The shear stress in low SF grains was not favorable for twinning and twins occurred in the vicinity of stress accumulation. Twinning activities in the same grain varied on different layers. It was attributed to the stress fluctuation derived from grain environment changes.

cond-mat.mtrl-sci↗

arXiv2022

OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face Representation

Face representation in the wild is extremely hard due to the large scale face variations. To this end, some deep convolutional neural networks (CNNs) have been developed to learn discriminative feature by designing properly margin-based losses, which perform well on easy samples but fail on hard samples. Based on this, some methods mainly adjust the weights of hard samples in training stage to improve the feature discrimination. However, these methods overlook the feature distribution property which may lead to better results since the miss-classified hard samples may be corrected by using the distribution metric. This paper proposes the hard samples guided optimal transport (OT) loss for deep face representation, OTFace for short. OTFace aims to enhance the performance of hard samples by introducing the feature distribution discrepancy while maintain the performance on easy samples. Specifically, we embrace triplet scheme to indicate hard sample groups in one mini-batch during training. OT is then used to characterize the distribution differences of features from the high level convolutional layer. Finally, we integrate the margin-based-softmax (e.g. ArcFace or AM-Softmax) and OT to guide deep CNN learning. Extensive experiments are conducted on several benchmark databases. The quantitative results demonstrate the advantages of the proposed OTFace over state-of-the-art methods.

cs.CV↗