In the pursuit of high-performance organic electrochromic (OEC) materials that transition from transparent to deep colors, we design and synthesize a series of copolymers─termed MeTmPhFn─composed of TPA and fluorene with triethylene glycol and ester groups incorporated. This is achieved through gradient tuning of monomer ratios via Suzuki copolymerization. Five of these polymers exhibit excellent film-forming properties, and their electrochromic devices demonstrate remarkable pale blue-to-reddish brown switching behavior. Notably, benefiting from the suitable continuity and moderate porosity of MeT0.5PhF0.5 film, the electrochromic device based on MeT0.5PhF0.5 showcases rapid reversible switching times of 2.5 s for coloring and 1.3 s for bleaching, highlighting its exceptional responsiveness. This device achieves the highest contrast of 58.7% at 465 nm, paired with impressive cycling stability, retaining 93.7% of its initial contrast ratio after 1440 cycles, and exhibits a high coloration efficiency of 619 cm2 C–1. This study presents an effective approach for designing and synthesizing high-performance electrochromic copolymers that effectively transition from colorless to vibrant hues, paving the way for advanced applications in smart windows and display technologies.
The emergence of “superbugs” is not only problematic and potentially lethal for infected subjects but also poses serious challenges for the healthcare system. Although existing antibacterial agents have been effective in some cases, the side effects and biocompatibility generally present difficulties. The development of new antibacterial agents is therefore urgently required. In this work, we have adapted a strategy for the improvement of poly(hexamethylene guanidine) hydrochloride (PHMG), a common antibacterial agent. This involves copolymerization of separate monomer units in varying ratios to find the optimum ratio of the hydrocarbon to guanidine units for antibacterial activity. A series of these copolymers, designated as PGB, was synthesized. By varying the guanidine/hydrophobic ratio and the copolymer molecular weight, a structure-optimized PGB was identified that showed broad-spectrum antibacterial activity and excellent biocompatibility in solution. In an antibacterial assay, the copolymer with the optimum composition (hydrophobic unit content 25%) inhibited >99% Staphylococcus aureus and was compatible with mammalian cells. A polyurethane emulsion containing this PGB component formed transparent, flexible films (PGB-PU films) on a wide range of substrate surfaces, including soft polymers and metals. The PGB-PU films showed excellent bacteriostatic efficiency against nosocomial drug-resistant bacteria, such as Pseudomonas aeruginosa and methicillin-resistant S. aureus (MRSA). It is concluded that our PGB polymers can be used as bacteriostatic agents generally and in particular for the design of antibacterial surfaces in medical devices.
The conventional method for effective or toxic chemical substance identification of multicomponent herbal medicine is based on single component separation, which is time-consuming, labor intensive, inefficient, and neglects the interaction and integrity among the components; therefore, it is necessary to find an alternative routine to evaluate the components more efficiently and scientifically. In this study, sodium aescinate injection (SAI), obtained from different manufacturers and prepared as “components knockout” samples, was chosen as the case study. The chemical fingerprints of SAI were obtained by high-performance liquid chromatography to provide the chemical information. The effectiveness and irritation of each sample were evaluated using anti-inflammatory and irritation tests, and then “Gray correlation” analysis (GCA) was applied to rank the effectiveness and irritability of each component to provide a preliminary judgment for product optimization. The prediction model of the proportions of the expected components was constructed using the artificial neural network. The results of the GCA showed that the irritation sorting of each SAI component was in the order of B > A > G > J > I > H > D > F > E > C and the effectiveness sorting of SAI components was in the order of D > C > B > A > F > E > H > I > G > J; the predictive proportion of SAI was optimized by the BP neural network as A: B: C: D: E: F = 0.7526: 0.5005: 5.4565: 1.4149: 0.8113: 1.0642. This study provided a scientific, accurate, reliable, and efficient approach for the proportion optimization of multicomponent drugs, which has a good prospect of popularization and application in product upgrading and development of herbal medicine.
危险品化学品经营许可证（带存储）