Language models exhibit a profound aptitude for addressing multimodal and multidomain challenges, a competency that eludes the majority of off-the-shelf machine learning models. Consequently, language models hold great potential for comprehending the intricate interplay between material compositions and diverse properties, thereby accelerating material design, particularly in the realm of polymers. While past limitations in polymer data hindered the use of data-intensive language models, the growing availability of standardized polymer data and effective data augmentation techniques now opens doors to previously uncharted territories. Here, we present a revolutionary model to enable rapid and precise prediction of Polymer properties via the power of Natural language and Chemical language (PolyNC). To showcase the efficacy of PolyNC, we have meticulously curated a labeled prompt-structure-property corpus encompassing 22,970 polymer data points on a series of essential polymer properties. Through the use of natural language prompts, PolyNC gains a comprehensive understanding of polymer properties, while employing chemical language (SMILES) to describe polymer structures. In a unified text-to-text manner, PolyNC consistently demonstrates exceptional performance on both regression tasks (such as property prediction) and the classification task (polymer classification). Simultaneous and interactive multitask learning enables PolyNC to holistically grasp the structure-property relationships of polymers. Through a combination of experiments and characterizations, the generalization ability of PolyNC has been demonstrated, with attention analysis further indicating that PolyNC effectively learns structural information about polymers from multimodal Inputs. This work provides compelling evidence of the potential for deploying end-to-end language models in polymer research, representing a significant advancement in the AI community's dedicated pursuit of advancing polymer science.

A series of macroporous adsorbents based on polyvinyl alcohol–formaldehyde (PVF) sponges was prepared using redox-initiated grafting polymerization of acrylamide (AM) followed by hydrolysis under alkaline conditions. The as-prepared sponges display average pore sizes in the range of 60–90 μm and interconnected pores with a porosity of approximately 90%. Elementary analysis confirmed that the amide groups in AM grafted PVF (PVF-g-GAM) have been rapidly converted into sodium carboxylate and the hydrolysis degree (HD) reaches approximately 65.5% within 6 h. The PVF-g-GAA (hydrolyzed PVF-g-GAM) sponges possess excellent water absorption performance with the ability to reach water absorption equilibrium within a few seconds and a saturated absorption capacity more than 300 g g⁻¹. Importantly, the PVF-g-GAA can be used as adsorbents to remove toxic metal ions, such as Cu²⁺, Pb²⁺ and Cd²⁺, in wastewater efficiently due to the existence of abundant carboxylate groups in the abovementioned network. In single metal ion systems, the sponges could reach adsorption equilibrium within 10 min and the adsorption kinetics fit well with a pseudo-second order kinetic equation. The influence of pH and types of metal ions on the adsorption capacities have also been investigated extensively. PVF-g-GAA-20 displays high adsorption performances with maximum adsorption capacities for Cu²⁺, Pb²⁺ and Cd²⁺ up to 2.50, 3.20 and 3.15 mmol g⁻¹ at pH 5.11, respectively. The equilibrium adsorption isotherm demonstrated that the adsorption equilibrium of the PVF-g-GAA-20 for Cu²⁺, Pb²⁺ and Cd²⁺ ions follows the Langmuir isotherm model very well with maximum adsorption capacities approximately 4.00, 3.97 and 3.34 mmol g⁻¹, respectively. In the binary metal ion coexistence systems, including Cu²⁺/Pb²⁺, Cu²⁺/Cd²⁺ and Pb²⁺/Cd²⁺ mixtures, PVF-g-GAA-20 displayed excellent absorption selectivity for Cu²⁺ and Pb²⁺ and the values of and were 13.4 and 7.14, respectively. It can be noted that the adsorption capacities for the abovementioned three metal ions slightly decreased with the variation of ionic strength in the range of 0.01–0.08 M. The sample also exhibited a quick desorption procedure of less than 10 min and excellent reusability of atleast six re-cycles. The adsorption mechanism was also discussed. The PVF-g-GAA sponges are definitely ideal adsorbents for removing/separating toxic metal ions in waste/polluted water bodies.

In this study, polyimide fibers at different stages of imidization were characterized by TGA, DSC, and FTIR. The imidization degree (ID) calculated by TGA was based on the weight loss of each sample, which was caused by the imidization of residual amic acid groups. The results of TGA showed good regularity with the thermal treatment temperature of the PI fibers. For DSC, the ID was calculated based on the area of endothermal peak of each sample. Compared with TGA, DSC showed a relatively higher value because the endothermal peak was reduced by the exothermic re-formation of polyamic acid which may be partially degraded during thermal treatment. The IDs obtained by the FTIR spectra generally showed poorer regularities than those obtained by both TGA and DSC, especially for the results calculated using the 730 cm⁻¹ band. Based on the 1350 cm⁻¹ band, the obtained IDs showed better agreement with the TGA or DSC results. The results obtained by these three methods were compared and analyzed. The ID obtained by TGA showed much more reliability among these three methods.