| 1. |
- Gao, C., et al.
(författare)
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Plasticization of a protein-based film by glycerol : A spectroscopic, mechanical, and thermal study
- 2006
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Ingår i: Journal of Agricultural and Food Chemistry. - : American Chemical Society (ACS). - 0021-8561 .- 1520-5118. ; 54:13, s. 4611-4616
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Tidskriftsartikel (refereegranskat)abstract
- Kafirin, the seed storage protein of the cereal sorghum, is highly homologous with the maize storage protein zein. The effects of plasticisation of a kafirin film by glycerol in the absence of water were examined by a combination of spectroscopic (NMR and infrared), rheological, and calorimetric methods. The results suggest that at low glycerol levels the glycerol is absorbed onto and possibly into the protein. Increasing the level of glycerol increases the motion of the protein and changes the protein conformation. There are corresponding changes of the mechanical properties of protein films. At 40% (w/w) of glycerol, two glass transition temperatures were observed, one of which corresponded to the glass transition temperature of pure glycerol. This result indicates that at this level of plasticizer there are sufficient glycerol/glycerol interactions occurring to allow a separate glass formation process for glycerol. © 2006 American Chemical Society.
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| 2. |
- Chen, T., et al.
(författare)
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An Improved Multimodal Trajectory Prediction Method Based on Deep Inverse Reinforcement Learning
- 2022
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 11:24
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Tidskriftsartikel (refereegranskat)abstract
- With the rapid development of artificial intelligence technology, the deep learning method has been introduced for vehicle trajectory prediction in the internet of vehicles, since it provides relative accurate prediction results, which is one of the critical links to guarantee security in the distributed mixed-driving scenario. In order to further enhance prediction accuracy by making full utilization of complex traffic scenes, an improved multimodal trajectory prediction method based on deep inverse reinforcement learning is proposed. Firstly, a fused dilated convolution module for better extracting raster features is introduced into the existing multimodal trajectory prediction network backbone. Then, a reward update policy with inferred goals is improved by learning the state rewards of goals and paths separately instead of original complex rewards, which can reduce the requirement for predefined goal states. Furthermore, a correction factor is introduced in the existing trajectory generator module, which can better generate diverse trajectories by penalizing trajectories with little difference. Abundant experiments on the current popular public dataset indicate that the prediction results of our proposed method are a better fit with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method. © 2022 by the authors.
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| 3. |
- Gao, Jiajia, et al.
(författare)
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Crystallography as Forensic Tool for Understanding Electrolyte Degradation in Dye-sensitized Solar Cells
- 2017
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Ingår i: ChemistrySelect. - : WILEY-V C H VERLAG GMBH. - 2365-6549. ; 2:4, s. 1675-1680
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Tidskriftsartikel (refereegranskat)abstract
- The precipitation of solid compounds from model electrolytes for liquid dye-sensitized solar cells has a story to tell regarding decomposition processes to be expected in such systems. Of course, the crystal lattice energy for a specific crystalline compounds plays a role in what compound that will eventually precipitate, but the compounds nevertheless serve as indicators for what type of processes that take place in the solar cell electrolytes upon ageing. From the compounds isolated in this study we learn that both ligand exchange processes, double-salt precipitation and oxidation are degradation processes that should not be overlooked when formulating efficient and stable electrolytes for this type of electrochemical system.
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| 4. |
- Gao, Yu, et al.
(författare)
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Analysis of gas chromatography/mass spectrometry data for catalytic lignin depolymerization using positive matrix factorization
- 2018
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Ingår i: Green Chemistry. - : Royal Society of Chemistry (RSC). - 1463-9262 .- 1463-9270. ; 20:18, s. 4366-4377
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Tidskriftsartikel (refereegranskat)abstract
- Various catalytic technologies are being developed to efficiently convert lignin into renewable chemicals. However, due to its complexity, catalytic lignin depolymerization often generates a wide and complex distribution of product compounds. Gas chromatography/mass spectrometry (GC-MS) is a common analytical technique to profile the compounds that comprise lignin depolymerization products. GC-MS is applied not only to determine the product composition, but also to develop an understanding of the catalytic reaction pathways and of the relationships among catalyst structure, reaction conditions, and the resulting compounds generated. Although a very useful tool, the analysis of lignin depolymerization products with GC-MS is limited by the quality and scope of the available mass spectral libraries and the ability to correlate changes in GC-MS chromatograms to changes in lignin structure, catalyst structure, and other reaction conditions. In this study, the GC-MS data of the depolymerization products generated from organosolv hybrid poplar lignin using a copper-doped porous metal oxide catalyst and a methanol/dimethyl carbonate co-solvent was analyzed by applying a factor analysis technique, positive matrix factorization (PMF). Several different solutions for the PMF model were explored. A 13-factor solution sufficiently explains the chemical changes occurring to lignin depolymerization products as a function of lignin, reaction time, catalyst, and solvent. Overall, seven factors were found to represent aromatic compounds, while one factor was defined by aliphatic compounds.
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