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In the field of material informatics, artificial neural networks (ANNs) contribute to the investigation of the processing‐structure‐properties‐performance relationship of materials. This inspires us to leverage the capabilities of ANNs to decode properties of hydrogels, thereby customizing these active materials for sensors or actuators. In the current work, we introduce an approach to predict discrete swelling states of temperature‐responsive hydrogels, especially PNIPAAm, based on their synthesis parameters, utilizing ANN models. To build the database, we analyze literature on temperature‐responsive hydrogels and compile essential synthesis parameters. The corresponding data points related to these synthesis parameters are then extracted. We propose different variants of ANN models and compare their accuracy on the acquired dataset. The selected model can predict the swelling states of hydrogel samples within the test dataset with relative prediction error of 0.11. This approach is applied to predict the expected properties. Subsequently, the hydrogels can be synthesized, and their properties can be experimentally verified. Our approach can be extended to other types of hydrogels and in the prediction of additional properties. The identified synthesis parameters serve as a valuable foundation for the expansion of the database with further literature resources. An enriched database will enhance the performance of the data‐driven model, thereby improving its predictive capabilities. [ABSTRACT FROM AUTHOR]

Metallic nanoparticles are of growing interest due to their broad applications. This study presents the green synthesis of zinc oxide (ZnO) nanoparticles (ZnNPs) using Ganoderma Lucidum mushroom extract, characterized by DLS, SEM, XRD, and FTIR spectroscopy analyses. The synthesis parameters, including extract/salt ratio and mixing time, significantly influenced nanoparticle yield, size, and polydispersity, with longer mixing times leading to larger, more varied particles. Specifically, the sizes of ZnNPs synthesized at a 1:1 extract/ZnCl2 ratio after 3 h and 24 h were 90.0 nm and 243.3 nm, with PDI values of 48.69% and 51.91%, respectively. At a 1:2 ratio, the sizes were 242.3 nm at 3 h (PDI: 43.19%) and a mixture of 1.5 nm, 117.4 nm, and 647.9 nm at 24 h (PDI: 2.72%, 10.97%, and 12.43%). Polymer films incorporating PVA, chitosan, and ZnNPs were analyzed for their morphological, spectroscopic, and mechanical properties. Chitosan reduced tensile strength and elongation due to its brittleness, while ZnNPs further increased film brittleness and structural degradation. A comparison of the tensile strength of films A and C revealed that the addition of chitosan to the PVA film resulted in an approximately 10.71% decrease in tensile strength. Similarly, the analysis of films B1 and B2 showed that the tensile strength of the B2 film decreased by 10.53%. Swelling tests showed that ZnNPs initially enhanced swelling, but excessive amounts led to reduced capacity due to aggregation. This pioneering study demonstrates the potential of Ganoderma Lucidum extract in nanoparticle synthesis and provides foundational insights for future research, especially in wound dressing applications. [ABSTRACT FROM AUTHOR]

Abstract In the field of material informatics, artificial neural networks (ANNs) contribute to the investigation of the processing‐structure‐properties‐performance relationship of materials. This inspires us to leverage the capabilities of ANNs to decode properties of hydrogels, thereby customizing these active materials for sensors or actuators. In the current work, we introduce an approach to predict discrete swelling states of temperature‐responsive hydrogels, especially PNIPAAm, based on their synthesis parameters, utilizing ANN models. To build the database, we analyze literature on temperature‐responsive hydrogels and compile essential synthesis parameters. The corresponding data points related to these synthesis parameters are then extracted. We propose different variants of ANN models and compare their accuracy on the acquired dataset. The selected model can predict the swelling states of hydrogel samples within the test dataset with relative prediction error of 0.11. This approach is applied to predict the expected properties. Subsequently, the hydrogels can be synthesized, and their properties can be experimentally verified. Our approach can be extended to other types of hydrogels and in the prediction of additional properties. The identified synthesis parameters serve as a valuable foundation for the expansion of the database with further literature resources. An enriched database will enhance the performance of the data‐driven model, thereby improving its predictive capabilities.

Polyacrylamide hydrogel was investigated to improve its water retention capacity. Among the several hydrogel samples prepared, a sample with ∼0.55 % cross-linker was found sufficiently rigid with high water content. The drying and rehydration cycles induced the appearance and disappearance of cracks respectively within the hydrogel and were confirmed by FESEM analysis. The effect of mili-patterns on hydrogel surface was studied and was found that the patterns enhanced the duration of water retention by ∼60 %. Even after 100 h of drying of a swelled hydrogel, the one with the pattern retained sufficient water so that its length remained ∼35 % longer compared to a non-patterned flat hydrogel. These findings were discussed in light of the Kelvin equation and were supported by the time-resolved Raman spectra that ensured the presence of the bound water for a longer duration. Based on the intensity of O–H stretching, it was inferred that patterned hydrogel exhibited water retention efficiency (RE) of ∼97 %, whereas, a flat non-patterned hydrogel had RE merely of ∼70 % after 3 cycles of drying and rehydration. [Display omitted] • Milli-patterns on PAAm hydrogel surface enhanced water retention duration by ∼60 %. • Length of patterned hydrogel remains 35 % longer than flat hydrogel while drying. • Kelvin eq. and Raman spectra support higher water retention by patterned hydrogel. • Patterned hydrogel showed retention efficiency (RE) of ∼97 %. [ABSTRACT FROM AUTHOR]