Shanxi Academy of Building Research

Abstract Poly(N-isopropylacrylamide) (PNIPAM) hydrogels are potential wound dressings due to their lower critical solution temperature (LCST) of 32 °C, close to physiological temperature, enabling injectable or spreadable formulations that quickly form a gel on irregular wounds while withstanding tissue motion. These systems aim to balance skin-like mechanical integrity, strong dry and wet adhesion, and deliver stability after placement. This review summarizes the impact of the LCST driven phase transition on water content, pore structure, stiffness, and interfacial hydration and how these effects regulate adhesion strength and failure mode. It compares major mechanical reinforcement strategies in PNIPAM-based dressings including hybrid nanocomposites, double network (DN) architectures, dynamic or sacrificial crosslinking strategies that enhance toughness, stretchability, and recovery. It also describes multifunctional systems incorporating antibacterial activity, hemostasis, sensing, photothermal response, or controlled drug release with emphasis on their effect on mechanics and adhesion. Since adhesion in soft hydrogels is strongly correlated with viscoelastic behavior, the review identifies rheological measures (G′/G ̋ trends, shear thinning, yield behavior, and recovery) as measures of injectability and wet adhesion stability. Lastly, essential translational barriers are defined, including limited biodegradability, additive cytotoxicity, sterilization, durability in exudate and motions, and standardized testing and reporting to facilitate reliable comparison and clinical relevance.

Abstract Polyglyphanodontian lizards were one of the dominant groups among squamates during the Late Cretaceous. They were widely distributed and exhibited a great range of body sizes and high morphological variation. The Asian taxa are preserved mostly as complete or nearly complete skulls or skeletons, therefore providing substantial anatomical information for the clade. Here, we describe a new polyglyphanodontian genus and species from the Upper Cretaceous of Ganzhou Basin, Jiangxi Province, China. The new taxon is diagnosed by its unique dentition, with complex posterior teeth having mesial, distal and lingual expansions, connected by crests and with depressions between. The phylogenetic analyses support an assignment of the new taxon to Polyglyphanodontia, forming an unresolved clade with Polyglyphanodon, Gilmoreteius, Adamisaurus, Tianyusaurus and Yechilacerta, or being a sister taxon to either Adamisaurus or a small clade of Tianyusaurus and Yechilacerta. The cranial and dental morphology suggests the new lizard was omnivorous, contrasting with the specialized herbivory of Tianyusaurus and Yechilacerta from the same region. Moreover, the new lizard is the first polyglyphanodontian taxon from Asia with a complex dentition, which shows similarities to that of some North American taxa, and this may suggest the evolutionary history of Polyglyphanodontia is more complicated than previously thought.

ABSTRACT The rapid progress of metal halide perovskite solar cells (PSCs) is hampered by intrinsic defects that lead to efficiency losses and poor stability. Surface passivation, particularly through the formation of quasi‐2D perovskites, offers a promising avenue to mitigate these challenges. Here, we developed a polarity‐enhanced surface passivation strategy by introducing fluorine atoms onto the passivation molecules. A stronger polarity could enhance interlayer interactions, reduce trap densities, improve interface charge transfer, suppress non‐radiative recombination, and improve energy band alignment at the interface. As a result, the S‐P‐F‐MBAI passivation process yielded a device with a PCE of 25.05% (23.67% for the flexible device). Benefiting from the enhanced structural stability conferred by strong polarity and the increased hydrophobicity imparted by aromatic backbone and fluorine atom, the S‐P‐F‐MBAI‐passivated device exhibited significantly improved environmental and performance stability, keeping more than 95% and 80% of its original PCE after 1000 h under environmental conditions and MPP tracking conditions with continuous illumination, respectively. This work underscores the importance of rational molecular design, leveraging polarity and hydrophobic functionalization, for realizing high‐efficiency and stable PSCs.

The foundation of successful mineral exploration is precise bauxite horizon demarcation and grade estimation. Although core analysis is the industry standard method, it is costly, labor-intensive, and has a relatively low processing capacity. To overcome these limitations, this study constructed an Extreme Gradient Boosting (XGBoost) classifier based on the logging parameters of natural gamma logging (GR), natural gamma spectroscopy logging (GGL), three-lateral logging (LL3), and compensated density logging (CDN) in order to achieve the automation of ore layer identification and grade prediction. The karst-type bauxite in Lvliang, Shanxi, was used to validate the research. The model was trained using the data from four wells in Shenjiazhuang. The trained model was directly applied to a blind well in Xingxian without parameter adjustment. Strong cross-site generalization was demonstrated by horizon recognition, which achieved 98.18% accuracy, 96.62% precision, 91.49% recall, and an F1 score of 93.99%. Based on the Al/Si ratio (A/S) and the content of Al2O3, the grade prediction classifies the samples into three grades: high-, medium-, and low-grade. The Mean Absolute Errors (MAEs) for the prediction of high- and medium-grade subsets of Al2O3 were 0.906 and 1.643, respectively, and those for A/S were 1.224 and 1.146, respectively. And the coefficient of determination (R2) for each grade level was greater than 0.8. These results support XGBoost’s field applicability and resilience for intelligent bauxite exploration.

Acid mine drainage (AMD), containing excessive sulfates and heavy metals, poses a severe threat to the environment and human health. In this study, a laboratory-scale column device using varying ratios of coarse sand to carbon steel slag (CS/CSS) as the filled material was designed to efficiently remove typical pollutants in AMD. Results revealed that the removal rates of SO42−, Fe, Mn, and Zn, as well as the pH value of the solution were influenced by the ratio of CS/CSS. The highest removal rates of SO42−, Fe, Mn, and Zn were 92%, 99.99%, 99.98%, and 99.99%, respectively, for CS/20%CSS. Additionally, XPS and SEM results indicated that sulfate (SO42−) existed in that groups associated with CaSO4 and Fe-sulfate complexes, while Fe, Mn, and Zn were simultaneously attached to the surface of the materials in the form of ion exchange, hydroxides, and co-precipitates. A modified sequential extraction method (BCR) results indicated that the distribution patterns of Fe, Mn, and Zn varied with increasing CSS content. Notably, when the CSS content in the column reached 20%, the metal ions were predominantly adsorbed in the top layer. The Risk Assessment Code values for Fe exhibited a low risk to the environment. These results highlight the importance of optimizing the CSS content in the permeable reactive barrier (PRB) to balance removal efficiency and permeability. Consequently, it is recommended that the content of CSS in the PRB should be maintained below 10% to ensure optimal performance and permeability. This study provides a foundation for optimizing PRB filled material design to enhance its efficiency and environmental sustainability in AMD treatment.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-26562-4.

Identification of structural wooden components is crucial for heritage architecture conservation as it elucidates the utilization patterns of forest resources and evolution of human civilization. This paper proposes a computer vision-based in situ identification method for wooden components of Chinese heritage architectures, using a dataset comprising 4050 images from 63 components of nine buildings. The optimal algorithm, RepLKNet, trained on coniferous xylarium specimens, achieves 96.67% identification accuracy, with 98.33%, 93.33%, and 90% precision at 50%, 70%, and 90% confidence thresholds, respectively. A minimum sample size of 25 species and 1500 images per genus ensures test accuracy >90%. Impact of structural deterioration (decay and cracks) on accuracy is also evaluated. Cracks significantly affect the wood recognition accuracy of historical components. Performance degrades significantly when cracks span >30% of the image. Latewood integrity is also critical to identification. The proposed method advances structural preservation strategies and preventive maintenance practices in heritage architecture.

Abstract. Murals constitute an invaluable component of cultural heritage, encapsulating profound artistic and historical significance. Hyperspectral remote sensing, as a non-destructive testing technique, offers an effective means for analysing and identifying mural pigments. We proposed a spectral unmixing method based on the negative logarithm of spectral reflectance, aimed at improving the accuracy of quantitative analysis for composite pigments in murals. We obtained three sets of mixed spectra: cinnabar-orpiment, azurite-orpiment, and azurite-malachite. The original reflectance was converted into negative logarithmic values, and linear models were applied for unmixing. Supervised unmixing was conducted in the experiment to evaluate the model’s precision, with the unmixing process carried out using known endmembers. The unmixing accuracy was quantified using the Root Mean Square Error, which compares the estimated abundances of all endmembers with actual values. Additionally, a comparison was made between the negative logarithmic transformation and the commonly used transformations of original reflectance, first derivative, and second derivative. The result show that the logarithmic hyperspace linear model simplifies spectral unmixing complexity, enhancing the accuracy of linear unmixing models in addressing mixed pigment unmixing problems. To validate the effectiveness of the proposed method for real murals, the improved model was applied to the hyperspectral data unmixing analysis of murals collected by the Yungang Academy, located in Shanxi Province, China. Experimental findings demonstrate that, compared to traditional methods, the improved unmixing approach more precisely represents the pigment distribution in the murals. This study offers new ideas and methods for the digital preservation and restoration of mural composite pigments.

Aging plays an important role in the development of breast cancer (BRCA); however, the specific underlying mechanism remains unclear. In this study, consensus clustering was initially used to classify BRCA subtypes. Thereafter, differentially expressed genes were identified within BRCA datasets and across subtypes, with overlapping genes being selected for further analysis. Cox regression analysis was used to identify key genes to construct a risk model for predicting prognosis. Furthermore, GSVA was used for pathway enrichment analysis, the infiltration levels of various immune cells were assessed, and the correlation between immune checkpoints and risk scores was analyzed. Finally, the expression levels of the key genes were validated through qRT-PCR. In addition, we also investigated the role of CXCL14 overexpression in the proliferation, invasion, and migration of MDA-MB-231 and MCF-7 cells. Human peripheral blood lymphocytes were co-cultured with MDA-MB-231 and MCF-7 cells at a cell density ratio of 2:1, and the proportion of live CD8+ T cells was measured by flow cytometry. Cox analysis revealed JCHAIN, KRT15, and CXCL14 as key prognosis-related genes; therefore, these genes were used to construct the risk model. In addition, age and stage were identified as independent prognostic factors. GSVA showed pathway enrichment in various risk groups, with the infiltration levels of 27 immune cell types being correlated with the risk score. The high-risk group exhibited downregulation of LAG3, CD274, CTLA4, and PDCD1. qRT-PCR validated the downregulation of JCHAIN and CXCL14 in a specific subgroup. CXCL14 overexpression significantly inhibited the proliferation, invasion, and migration capabilities of breast cancer cells. Furthermore, CXCL14 further suppressed tumor occurrence and development by activating CD8+ T cell-mediated immune mechanisms. In this study, the aging-related genes JCHAIN, KRT15, and CXCL14 were identified as key biomarkers for predicting the prognosis of BRCA.