Ultrasonication improves the structures and physicochemical properties of Cassava starch films containing acetic acid

Cassava starch films are fabricated with acetic acid treatment and ultrasonication. Different ultrasound power levels from 200 to 750 W are used and the effects of ultrasonication on the morphology, microstructures, and properties of the starch–acetic acid films are investigated. Scanning electron microscopy shows a cohesive and compact structure of the films resulting from ultrasonication. X‐ray diffraction analysis reveals that the crystalline index is decreased by acid treatment and increased by ultrasonication. The tensile strength and elongation at break of the films first increase and then decrease with increasing ultrasound power level. Ultrasonication also results in higher opacity, higher water barrier performance, and lower water adsorption of the films. Thus, the results show that ultrasonication can be used as a simple and efficient way to modify the morphology, microstructure, and performance of starch–acetic acid films to better meet the application needs

Morphology and properties of thermal/cooling-gel bi-phasic systems based on hydroxypropyl methylcellulose and hydroxypropyl starch

The miscibility between two gels with largely different gelation behaviors is an interesting topic both scientifically and practically. This paper reports a novel bi-phasic system based on two natural polymers, hydroxypropyl methylcellulose (HPMC) which has a thermal gelation behavior, and hydroxypropyl starch (HPS) which has a cooling gelation property. While both biopolymers have the same glucose unit grafted with propylene oxide, and are compatible to a certain degree, they were observed immiscible because of their different gelation behaviors. The immiscibility of these two compatible polymers could result in special structures leading to different blend film properties. Regarding this, the morphology, thermal transition, mechanical properties and oxygen barrier property could be well tailored by the ratio of two biopolymers and the environmental conditions. The knowledge obtained from this work could be useful for understanding other similar systems with desirable structure and properties

Understanding the multi-scale structure and digestibility of different waxy maize starches

This work concerns different cultivars of waxy maize starch (WMS), from which a significant correlation between the multi-scale structure and the digestibility has been identified. WMSs show a typical A-type crystalline polymorph. The surface porosity of WMS granules facilitates their digestibility. In contrast, the in vitro digestion results indicate that the resistant starch (RS) content increased with higher contents of amylose, single helices, and surface short-range ordered structures. Resistant starch (RS) was found to be made up of single helices and perfect crystallites formed by the fraction of chains with a degree of polymerization (DP) between 13 and 24. Slowly digestible starch (SDS) consists of single helices. Rapidly digestible starch (RDS) is mainly composed of disordered molecular chains in the amorphous regions of starch. This work reveals the relationship between the multiscale structure and digestibility of different WMSs and can provide guidance for the application of WMSs in food or non-food fields

Improving the in vitro digestibility of rice starch by thermomechanically assisted complexation with guar gum

The effects of thermomechanical treatment and guar gum (GG) addition on the in vitro digestibility of rice starch have been investigated. Rice starch added with GG at concentrations of 0, 0.025, 0.05, 0.075, or 0.10 g/100 g (wet basis) was subject to a micro-extrusion process. The in vitro digestibility, predicted glycemic index (pGI), and multi-scale structures (granule, lamellar, crystalline, and molecular structures) were examined. Micro-extruded rice starch (MERS) with GG presented reduced digestion rate and pGI, a higher degree of structural ordering, and altered crystalline, single-helical and double-helical structures. Using Pearson correlation analysis, the relationships among extrusion, the molecular interaction and multi-scale structure, and the digestibility were established. The content of resistant components (RC) was positively correlated with crystallinity (r = 0.836, p < 0.05), fractal dimension (r = 0.966, p < 0.05), A-type crystallinity (r = 0.954, p < 0.01), V-type crystallinity (r = 0.987, p < 0.05), R 1045 / 1022 (r = 0.987, p < 0.05), single-helix content (r = 0.897, p < 0.05), and double-helix content (r = 0.991, p < 0.01); and was negatively correlated with pGI (r = −0.947, p < 0.05). Overall, this study showed that thermomechanical treatment assisted the complexation of GG with starch, which could be an effective means to improve the resistant properties of rice starch

Lamellar structure change of waxy corn starch during gelatinization by time-resolved synchrotron SAXS

In situ experiment of synchrotron small- and wide-angle X-ray scattering (SAXS/WAXS) was used to study the lamellar structure change of starch during gelatinization. Waxy corn starch was used as a model material to exclude the effect of amylose. The thicknesses of crystalline (d), amorphous (d) regions of the lamella and the long period distance (d) were obtained based on a 1D linear correlation function. The SAXS and WAXS results reveal the multi-stage of gelatinization. Firstly, a preferable increase in the thickness of crystalline lamellae occurs because of the water penetration into the crystalline region. Then, the thickness of amorphous lamellae has a significant increase while that of crystalline lamellae decreases. Next, the thickness of amorphous lamellae starts to decrease probably due to the out-phasing of starch molecules from the lamellae. Finally, the thickness of amorphous lamellae decreases rapidly, with the formation of fractal gel on a larger scale (than that of the lamellae), which gradually decreases as the temperature further increases and is related to the concentration of starch molecular chains. This work system reveals the gelatinization mechanism of waxy corn starch and would be useful in starch amorphous materials processing

Supramolecular structure of jackfruit seed starch and its relationship with digestibility and physicochemical properties

The influence of supramolecular structure on the physicochemical properties and digestibility of jackfruit seed starch (JSS) were investigated. Compared with maize and cassava starches (MS and CS), JSS had smaller granules and higher amylose content (JSS: 24.90%; CS: 16.68%; and MS: 22.42%), which contributed to higher gelatinization temperature (To: 81.11 °C) and setback viscosity (548.9 mPa s). From scanning electron microscopy, the digestion of JSS was observed mainly at the granule surface. Due to its higher crystallinity (JSS: 30.6%; CS: 30.3%; and MS: 27.4%) and more ordered semi-crystalline lamellae, JSS had a high RS content (74.26%) and melting enthalpy (19.61 J/g). In other words, the supramolecular structure of JSS extensively determined its digestibility and resistance to heat and mechanical shear treatment

The Nrf2-Keap1 pathway is activated by steroid hormone signaling to govern neuronal remodeling

10.1016/j.celrep.2021.109466Cell Reports36510946

Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network

State of charge (SOC) is one of the most important parameters in battery management systems, and the accurate and stable estimation of battery SOC for real-world electric vehicles remains a great challenge. This paper proposes a long short-term memory network based on grid search and cross-validation optimisation to estimate the SOC of real-world battery systems. The real-world electric vehicle data are divided into parking charging, travel charging, and finish charging cases. Meanwhile, the parameters associated with the SOC estimation under each operating condition are extracted by the Pearson correlation analysis. Moreover, the hyperparameters of the long short-term memory network are optimised by grid search and cross-validation to improve the accuracy of the model estimation. Moreover, the gaussian noise algorithm is used for data augmentation to improve the accuracy and robustness of SOC estimation under the working conditions of the small dataset. The results indicate that the absolute error of SOC estimation is within 4% for the small dataset and within 2% for the large dataset. More importantly, the robustness and effectiveness of the proposed method are validated based on operational data from three different real-world electric vehicles, and the mean square error of SOC estimation does not exceed 0.006. This paper aims to provide guidance for the SOC estimation of real-world electric vehicles

Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network

State of charge (SOC) is one of the most important parameters in battery management systems, and the accurate and stable estimation of battery SOC for real-world electric vehicles remains a great challenge. This paper proposes a long short-term memory network based on grid search and cross-validation optimisation to estimate the SOC of real-world battery systems. The real-world electric vehicle data are divided into parking charging, travel charging, and finish charging cases. Meanwhile, the parameters associated with the SOC estimation under each operating condition are extracted by the Pearson correlation analysis. Moreover, the hyperparameters of the long short-term memory network are optimised by grid search and cross-validation to improve the accuracy of the model estimation. Moreover, the gaussian noise algorithm is used for data augmentation to improve the accuracy and robustness of SOC estimation under the working conditions of the small dataset. The results indicate that the absolute error of SOC estimation is within 4% for the small dataset and within 2% for the large dataset. More importantly, the robustness and effectiveness of the proposed method are validated based on operational data from three different real-world electric vehicles, and the mean square error of SOC estimation does not exceed 0.006. This paper aims to provide guidance for the SOC estimation of real-world electric vehicles

Investigation on Battery Thermal Management Based on Enhanced Heat Transfer Disturbance Structure within Mini-Channel Liquid Cooling Plate

The battery thermal management system plays an important role in the safe operation of a lithium-ion battery system. In this paper, a novel liquid cooling plate with mini-channels is proposed and is improved with disturbance structures. First, an accurate battery heat generation model is established and verified by experiments. The error is less than 4%, indicating the heat generation power is reliable. Then, five designs are proposed first to determine the suitable number of disturbance structures, and plan 3 with five disturbance structures shows a satisfying performance in heat dissipation and flow field. Moreover, four layout plans are proposed, namely uniform, interlaced, thinning, and gradually denser distribution. Results show that plan 5 (uniform) achieves the best performance: the maximum average temperature is 36.33 &deg;C and the maximum average temperature difference is 0.16 &deg;C. At last, the orthogonal experiment and range analysis are adopted to optimize the structure parameters. Results show that the best combination is space between adjacent disturbance structures d1 = 20 mm, length d2 = 5 mm, width d3 = 1.5 mm, and tilt angle &beta; = 60&deg;