High-Performance One-Component Epoxy Adhesive Based on the Synergistic Effect of Lignin-Derived Triaryl-Imidazole and Phytic Acid.

Latent curing agents are essential in the formulation of one-component epoxy resins, yet they are seldom derived from fully biobased chemicals. In the present work, a fully biobased latent curing agent for epoxy resins (BIMPA) was produced by synthesizing an ionic complex of lignin-derived triaryl-imidazole (BIM) and phytic acid (PA). Benefiting from the synergistic effect of BIM and PA, the one-component epoxy resin, composed of BIMPA and commercially available E51, exhibits a storage stability of over 90 days. Upon heating, the ionic complex undergoes decomposition, liberating the active imidazole to cure the precursor. The resulting epoxy resins exhibited a flexural modulus of 3.09 GPa, a flexural strength of 107.47 MPa, a notched izod impact strength of 2.47 kJ/m3, and a shear strength of 41.02 MPa. The outcome can provide an effective supplement for the development of biobased epoxy resins.

[Optimization of prediction model for personalized water pills based on semantic analysis of traditional Chinese medicine materials].

This study aims to optimize the prediction model of personalized water pills that has been established by our research group. Dioscoreae Rhizoma, Leonuri Herba, Codonopsis Radix, Armeniacae Semen Amarum, and calcined Oyster were selected as model medicines of powdery, fibrous, sugary, oily, and brittle materials, respectively. The model prescriptions were obtained by uniform mixing design. With hydroxypropyl methylcellulose E5(HPMC-E5) aqueous solution as the adhesive, personalized water pills were prepared by extrusion and spheronizaition. The evaluation indexes in the pill preparation process and the multi-model statistical analysis were employed to optimize and evaluate the prediction model of personalized water pills. The prediction equation of the adhesive concentration was obtained as follows: Y_1=-4.172+3.63X_A+15.057X_B+1.838X_C-0.997X_D(adhesive concentration of 10% when Y_1<0, and 20% when Y_1>0). The overall accuracy of the prediction model for adhesive concentration was 96.0%. The prediction equation of adhesive dosage was Y_2=6.051+94.944X_A~(1.5)+161.977X_B+70.078X_C~2+12.016X_D~(0.3)+27.493X_E~(0.3)-2.168X_F~(-1)(R~2=0.954, P<0.001). Furthermore, the semantic prediction model for material classification of traditional Chinese medicines was used to classify the materials contained in the prescription, and thus the prediction model of personalized water pills was evaluated. The results showed that the prescriptions for model evaluation can be prepared with one-time molding, and the forming quality was better than that established by the research group earlier. This study has achieved the optimization of the prediction model of personalized water pills.

[Intelligent material classification of traditional Chinese medicine based on semantic analysis].

A method for material classification of traditional Chinese medicines based on the physical properties of powder has been established by our research group. This method involves pre-treatment of traditional Chinese medicine decoction pieces, powder preparation, and determination of physical properties, being cumbersome. In this study, the word segmentation logic of semantic analysis was adopted to establish the thesaurus and local standardized semantic word segmentation database with the macroscopic and microscopic characteristics of 36 model traditional Chinese medicines as the basic data. The physical properties of these medicines have been determined and the classification of these medicines is clear in the cluster analysis. A total of 55 keywords for powdery, fibrous, sugary, oily, and brittle materials were screened by association rules and the set inclusion and exclusion criteria, and the weights of the keywords were calculated. Furthermore, the algorithms of the keyword matching scores and the computation rules of the single or multiple material classification were established for building the intelligent model of semantic analysis for the material classification. The semantic classification results of the other 35 TCMs except Pseudostellariae Radix(multi-material medicine) agreed with the clustering results based on the physical properties of the powder, with an agreement rate of 97.22%. In model validation, the prediction results of semantic classification of traditional Chinese medicines were consistent with the clustering results based on the physical properties of powder, with an agreement rate of 83.33%. The results showed that the method of material classification based on semantic analysis was feasible, which laid a foundation for the development of intelligent decision-making technology for personalized traditional Chinese medicine preparations.

[Classification optimization of traditional Chinese medicine materials based on additive physical properties of powder].

According to the method of predicting the physical properties of oily powder based on the additive physical properties of Chinese medicinal powder, Dioscoreae Rhizoma and calcined Ostreae Concha with high sieve rate and good fluidity were mixed and crushed with Persicae Semen, Platycladi Semen, Raphani Semen, Ziziphi Spinosae Semen, and other typical oily materials with high fatty oil content in proportion to obtain 23 mixed powders. Fifteen physical properties such as bulk density, water absorption, and maximum torque force were measured, and the physical properties of typical oily powders were predicted. When the mixing and grinding ratio was in the range of 5∶1-1∶1, the r value in the correlation equation between the weighted average score of the mixed powder and the powder proportion ranged from 0.801 to 0.986, and the linearity was good, indicating that the method of predicting the physical properties of oily powder based on the additive physical properties of traditional Chinese medicine(TCM)powder was feasible. The results of cluster analysis showed that the classification boundaries of the five kinds of TCM materials were clear, and the similarity of the physical fingerprints of powdery and oily materials decreased from 80.6% to 37.2%, which solved the problem of fuzzy classification boundaries of powdery and oily materials due to the lack of representativeness of oily material model drugs. The classification of TCM materials was optimized, laying a foundation for optimizing the prediction model of the prescription of personalized water-paste pills.

[Allelopathy autotoxicity effects of aquatic extracts from rhizospheric soil on rooting and growth of stem cuttings in Pogostemon cablin].

OBJECTIVE: To study the allelopathy effects of aquatic extracts from rhizospheric soil on the rooting and growth of stem cutting in Pogostemon cablin, and to reveal its mechanism initially. METHODS: The changes of rhizogenesis characteristics and physic-biochemical during cutting seedlings were observed when using different concentration of aquatic extracts from rhizospheric soil. RESULTS: Aquatic extracts from rhizospheric soil had significant inhibitory effects on rooting rate, root number, root length, root activity, growth rate of cutting with increasing concentrations of tissue extracts; The chlorophyll content of cutting seedlings were decreased, but content of MDA were increased, and activities of POD, PPO and IAAO in cutting seedlings were affected. CONCLUSION: Aquatic extracts from rhizospheric soil of Pogostemon cablin have varying degrees of inhibitory effects on the normal rooting and growth of stem cuttings.