Characteristics of Damage to Brown Rice Kernels under Single and Continuous Mechanical Compression Conditions.

During the rice milling process, single and continuous compression occurs between brown rice and the processing parts. When the external load exceeds the yield limit of brown rice, brown rice kernels are damaged; with an increase in compression deformation or the extent of compression, the amount of damage to the kernels expands and accumulates, ultimately leading to the fracture and breakage of kernels. In order to investigate the mechanical compression damage characteristics of brown rice kernels under real-world working conditions, this study constructs an elastic-plastic compression model and a continuous damage model of brown rice kernels based on Hertz theory and continuous damage theory; the accuracy of this model is verified through experiments, and the relevant processing critical parameters are calculated. In this study, three varieties of brown rice kernels are taken as the research object, and mechanical compression tests are carried out using a texture apparatus; finally, the test data are analysed and calculated by combining them with the theoretical model to obtain the relevant critical parameters of damage. The results of the single compression crushing test of brown rice kernels showed that the maximum destructive forces Fc in the single compression of Hunan Early indica 45, Hunan Glutinous 28, and Southern Japonica 518 kernels were 134.77 ± 11.20 N, 115.64 ± 4.35 N, and 115.84 ± 5.89 N, respectively; the maximum crushing deformations αc in the single compression crushing test were 0.51 ± 0.04 mm, 0.43 ± 0.01 mm, and 0.48 ± 0.17 mm, respectively; and the critical average deformations αs of elasticity-plasticity deformation were 0.224 mm, 0.267 mm, and 0.280 mm, respectively. The results of the continuous compression crushing test of brown rice kernels showed that the critical deformations αd of successive compression damage formation were 0.224 mm, 0.267 mm, and 0.280 mm, and the deformation ratios δ of compression damage were 12.24%, 14.35%, and 12.84%. From the test results, it can be seen that the continuous application of compression load does not result in the crushing of kernels if the compression deformation is less than αd during mechanical compression. The continuous application of compressive loads can lead to fragmentation of the kernels if the compressive deformation exceeds αd; the larger the compression variant, the less compression is required for crushing. If the compression deformation exceeds αc, then a single compressive load can directly fragment the kernels. Therefore, the load employed during rice milling should be based on the variety of brown rice used in order to prevent brown rice deformation, which should be less than αd, and the maximum load should not exceed Fc. The results of this study provide a theoretical reference for the structure and parameter optimisation of a rice milling machine.

Surface-Enhanced Raman Scattering Based on Sb2S3 Microstructures via Femtosecond Laser Direct Writing.

The noble-metal-free surface-enhanced Raman scattering (SERS) substrates have gained significant attention due to their abundant sources, signal uniformity, biocompatibility, and chemical stability. However, the lack of controllable synthesis and fabrication methods for high-SERS-activity noble-metal-free substrates hinders their practical applications. In this study, we demonstrate the use of a femtosecond laser direct writing technique to precisely manipulate and modify microstructures, resulting in enhanced SERS signals from Sb2S3 nonmetal-oxide semiconductor materials. Compared with unpatterned Sb2S3 samples, the Sb2S3 microstructures exhibited up to a 16-fold increase in Raman scattering intensity. Interestingly, our results indicate that the femtosecond laser can induce a transformation in the crystalline state of Sb2S3 and significantly enhance the Raman spectrum signal within the Sb2S3 microstructures. This enhancement is also highly dependent on the period and depth of the microstructures, possibly due to the cavity effects, resulting in a stronger local field enhancement.

Research on rice starch gel preparation and crosslink network structure-rheological property based on direct-writing 3D printing.

Amylopectin and amylose components are natural polymers within rice starch granules, intertwined in specific conditions to form gel polymerized with pore crosslink network, has potential printing properties. In this study, a rice starch gel preparation scheme is proposed for stable properties, and starch granule phase transition mechanism is analyzed based on RVA test during preparation, it can be divided into four-stage, swelling, reacting, homogenizing and self-assembling stages. Gel surface tension and contact angle tested with starch concentration effect, a correlation is developed, reflecting a competition result to gel droplet macro-morphology between the intermolecular cohesion and crosslink network. SEM is used to reveal typical crosslink structures of different starch molecular component proportions, providing objective support for starch gel rheologic property change. Results indicate gel interior crosslink network formed under concentration 12 %, the gel with amylose 4.475 % presents better printing accuracy. Gel shear modulus positively correlated with amylose proportion. Japonica gel under 20 % is of higher viscosity and rapid reassembly ability after interior crosslink network is broken. Max dynamic viscosity is positively correlated with starch concentration. The study aims to provide theoretical and practical support for in-depth analysis of rice starch material application in direct-write 3D printing.

An optimized heating source for heat-not-burn tobacco products through synergistic combustion of tobacco stalk and graphite carbon with K2CO3 catalyst.

Waste tobacco stalk is blended with graphite carbon to form a composite carbon source, which is a promising external heating system for heat-not-burn tobaccos. In the current work, the effects of tobacco stalk amounts and the catalyst K2CO3 on the co-combustion characteristics (i.e., the ignition temperature, burnout temperature, etc.) of graphite carbon were investigated. As a result, the ignition temperatures of the blend were determined by the tobacco straw, while the burnout temperature of the samples was reduced by approximately 60 °C due to the addition of a tobacco stalk. After the addition of K2CO3, the ignition temperatures of the mixture were further decreased by 2-7 °C since the ignition temperature of the tobacco stalk was difficult to further reduce after K2CO3 addition. Meanwhile, the burnout temperature can be reduced by 76-106 °C because the presence of K2CO3 can significantly improve the combustion of graphite carbon. In addition, the kinetic analysis revealed that during the release of volatile matter, the oxidation process accords with the first-order kinetic model, while in the combustion process of mixed carbon source, the combustion process can be described by the third-order kinetic model.

Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built. Degree of bran layer remaining (DOR) datasets is established with image capturing and processing (grain color, texture, and shape features extraction). The mapping relationship between DOR and the DOM is in-depth analyzed. Rice grain DOR typical machine learning and deep learning prediction models are established. The results indicate that the optimized Catboost model can be established with cross-validation and grid search method, with the best accuracy improving from 84.28% to 91.24%, achieving precision 91.31%, recall 90.89%, and F1-score 91.07%. Shapley additive explanations analysis indicates that color, texture, and shape feature affect Catboost prediction accuracy, the feature importance: color > texture > shape. The YCbCr-Cb_ske and GLCM-Contrast features make the most significant contribution to rice milling quality prediction. The feature importance provides theoretical and practical guidance for grain DOM prediction model. PRACTICAL APPLICATION: Rice milling degree prediction and detection are valuable for rice milling process in practical application. In this paper, image processing and machine learning methods provide an automated, nondestructive, and cost-effective way to predict the quality of rice. The study may serve as a valuable reference for improving rice milling methods, retaining rice nutrition, and reducing broken rice yield.