Advanced Materials for Energy-Water Systems: The Central Role of Water/Solid Interfaces in Adsorption, Reactivity, and Transport.

The structure, chemistry, and charge of interfaces between materials and aqueous fluids play a central role in determining properties and performance of numerous water systems. Sensors, membranes, sorbents, and heterogeneous catalysts almost uniformly rely on specific interactions between their surfaces and components dissolved or suspended in the water-and often the water molecules themselves-to detect and mitigate contaminants. Deleterious processes in these systems such as fouling, scaling (inorganic deposits), and corrosion are also governed by interfacial phenomena. Despite the importance of these interfaces, much remains to be learned about their multiscale interactions. Developing a deeper understanding of the molecular- and mesoscale phenomena at water/solid interfaces will be essential to driving innovation to address grand challenges in supplying sufficient fit-for-purpose water in the future. In this Review, we examine the current state of knowledge surrounding adsorption, reactivity, and transport in several key classes of water/solid interfaces, drawing on a synergistic combination of theory, simulation, and experiments, and provide an outlook for prioritizing strategic research directions.

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.

Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as 'tumor' or 'normal'. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classification and image classification, respectively. This two-branch structure enables the model to learn more general features and thus reduce the risk of overfitting when learning sparsely annotated data. We incorporate the idea of consistency learning and self-training into the semi-supervised training strategy to take advantage of the unlabeled images. Trained on the BCSS dataset with only 25% of the images labeled (five patches for each labeled image), our proposed method achieved competitive performance compared to the fully supervised pixel-wise segmentation models. Experiments demonstrate that the proposed solution has the potential to reduce the burden of labeling histopathological images.

Enrichment and Distribution of Pb2+ Ions in Zwitterionic Poly(cysteine methacrylate) Brushes at the Solid-Liquid Interface.

Cysteine-based polyzwitterionic brushes have been prepared via a two-step route. First, poly(allyl methacrylate) (PAMA) brushes have been grown from the surface of silicon substrates using surface-initiated atom transfer radical polymerization. The obtained PAMA brushes with free pendant vinyl groups were further modified via radical thiol-ene addition reaction to attach l-cysteine moieties. Surface ζ potential investigations on pH-responsiveness of these poly(cysteine methacrylate) (PCysMA) brushes confirm their zwitterionic character at intermediate pH range, while at pH values either below pH 3.50 or above pH 8.59, they exhibit polyelectrolyte character. Under acid (pH < 3.50) or base (pH > 8.59) conditions, they possess either cationic or anionic character, respectively. In the zwitterionic region, these PCysMA brushes show positive surface ζ potential in the presence of Pb(CH3COO)2 solutions of various concentrations. The results are in line with microscopic investigations using anomalous X-ray reflectivity (AXRR) carried out along the absorption edge of Pb2+ ions. When the photon energies were varied around the absorption L3 edge of lead (13037 eV), the Pb2+ concentration normal to the silicon substrates, as a function of depth inside PCysMA brushes, could be revealed at the nanoscale. Both ζ potential and AXRR measurements confirm the enrichment of Pb2+ ions inside PCysMA brushes, indicating the potential of PCysMA to be used as a water purification material.

Evidence and Limits of Universal Topological Surface Segregation of Cyclic Polymers.

If you mix lines and circles, what happens at the edge of the mixture? The problem is simply stated, but the answer is not obvious. Twenty years ago it was proposed that a universal topological driving force would drive cyclic chains to enrich the surface of blends of linear and cyclic chains. Here such behavior is demonstrated experimentally for sufficiently long chains and the limit in molecular weight where packing effects dominate over the topological driving force is identified.

C-terminal elongation of NS1 of H9N2 influenza virus induces a high level of inflammatory cytokines and increases transmission.

H9N2 avian influenza viruses are enzootic around the world and can infect many different avian and mammalian hosts, including humans. Unlike the H9N2 viruses, which mainly originated in other countries and possess a non-structural protein 1 (NS1) of 230 aa, 98 % of the H9N2 viruses isolated in China lack the 13 aa at the C terminus of NS1 (217 aa in total). The biological significance of NS1 elongation remains elusive. To examine the effect of NS1 C-terminal elongation in the influenza virus, we used reverse genetics to generate a wt avian influenza H9N2 virus containing a 217 aa NS1 (H9N2NS1217) and two mutant viruses with elongated NS1s of 230 and 237 aa (H9N2NS1230 and H9N2NS1237). C-terminal elongation of NS1 did not have a significant impact on virus replication in Madin-Darby canine kidney cells or DF-1 cells. The three variants exhibited similar replicability in mice; however, the H9N2NS1230 and H9N2NS1237 variants exhibited an upregulation in the level of inflammatory cytokines. In addition, both the H9N2NS1230 and H9N2NS1237 viruses increased replication and induced a high level of inflammatory cytokines and transmission in chickens, compared with the wt virus. These findings suggest that the NS1 extension conferred a gain of fitness to some extent.

Twenty amino acids at the C-terminus of PA-X are associated with increased influenza A virus replication and pathogenicity.

The PA-X protein, arising from ribosomal frameshift during PA translation, was recently discovered in influenza A virus (IAV). The C-terminal domain 'X' of PA-X proteins in IAVs can be classified as full-length (61 aa) or truncated (41 aa). In the main, avian influenza viruses express full-length PA-X proteins, whilst 2009 pandemic H1N1 (pH1N1) influenza viruses harbour truncated PA proteins. The truncated form lacks aa 232-252 of the full-length PA-X protein. The significance of PA-X length in virus function remains unclear. To address this issue, we constructed a set of contemporary influenza viruses (pH1N1, avian H5N1 and H9N2) with full and truncated PA-X by reverse genetics to compare their replication and host pathogenicity. All full-length PA-X viruses in human A549 cells conferred 10- to 100-fold increase in viral replication and 5-8% increase in apoptosis relative to corresponding truncated PA-X viruses. Full-length PA-X viruses were more virulent and caused more severe inflammatory responses in mice. Furthermore, aa 233-252 at the C terminus of PA-X strongly suppressed co-transfected gene expression by ∼ 50%, suggesting that these terminal 20 aa could play a role in enhancing viral replication and contribute to virulence.

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.

PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficult to obtain a training set with multiple stains because the labeling of pathology images is very time-consuming and tedious. Therefore, in this paper, we proposed an unsupervised stain augmentation-based method for segmentation of glomerular instances. METHODS: In this study, we successfully realized the conversion between different staining methods such as PAS, MT and PASM by contrastive unpaired translation (CUT), thus improving the staining diversity of the training set. Moreover, we replaced the backbone of mask R-CNN with swin transformer to further improve the efficiency of feature extraction and thus achieve better performance in instance segmentation task. RESULTS: To validate the method presented in this paper, we constructed a dataset from 216 WSIs of the three stains in this study. After conducting in-depth experiments, we verified that the instance segmentation method based on stain augmentation outperforms existing methods across all metrics for PAS, PASM, and MT stains. Furthermore, ablation experiments are performed in this paper to further demonstrate the effectiveness of the proposed module. CONCLUSION: This study successfully demonstrated the potential of unsupervised stain augmentation to improve glomerular segmentation in pathology analysis. Future research could extend this approach to other complex segmentation tasks in the pathology image domain to further explore the potential of applying stain augmentation techniques in different domains of pathology image analysis.

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition.

BACKGROUND: Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function. METHODS: Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification. RESULTS: This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model. CONCLUSION: The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.

Expression site agnostic histopathology image segmentation framework by self supervised domain adaption.

MOTIVATION: With the sites of antigen expression different, the segmentation of immunohistochemical (IHC) histopathology images is challenging, due to the visual variances. With H&E images highlighting the tissue structure and cell distribution more broadly, transferring more salient features from H&E images can achieve considerable performance on expression site agnostic IHC images segmentation. METHODS: To the best of our knowledge, this is the first work that focuses on domain adaptive segmentation for different expression sites. We propose an expression site agnostic domain adaptive histopathology image semantic segmentation framework (ESASeg). In ESASeg, multi-level feature alignment encodes expression site invariance by learning generic representations of global and multi-scale local features. Moreover, self-supervision enhances domain adaptation to perceive high-level semantics by predicting pseudo-labels. RESULTS: We construct a dataset with three IHCs (Her2 with membrane stained, Ki67 with nucleus stained, GPC3 with cytoplasm stained) with different expression sites from two diseases (breast and liver cancer). Intensive experiments on tumor region segmentation illustrate that ESASeg performs best across all metrics, and the implementation of each module proves to achieve impressive improvements. CONCLUSION: The performance of ESASeg on the tumor region segmentation demonstrates the efficiency of the proposed framework, which provides a novel solution on expression site agnostic IHC related tasks. Moreover, the proposed domain adaption and self-supervision module can improve feature domain adaption and extraction without labels. In addition, ESASeg lays the foundation to perform joint analysis and information interaction for IHCs with different expression sites.

Unpaired virtual histological staining using prior-guided generative adversarial networks.

Fibrosis is an inevitable stage in the development of chronic liver disease and has an irreplaceable role in characterizing the degree of progression of chronic liver disease. Histopathological diagnosis is the gold standard for the interpretation of fibrosis parameters. Conventional hematoxylin-eosin (H&E) staining can only reflect the gross structure of the tissue and the distribution of hepatocytes, while Masson trichrome can highlight specific types of collagen fiber structure, thus providing the necessary structural information for fibrosis scoring. However, the expensive costs of time, economy, and patient specimens as well as the non-uniform preparation and staining process make the conversion of existing H&E staining into virtual Masson trichrome staining a solution for fibrosis evaluation. Existing translation approaches fail to extract fiber features accurately enough, and the decoder of staining is unable to converge due to the inconsistent color of physical staining. In this work, we propose a prior-guided generative adversarial network, based on unpaired data for effective Masson trichrome stained image generation from the corresponding H&E stained image. Conducted on a small training set, our method takes full advantage of prior knowledge to set up better constraints on both the encoder and the decoder. Experiments indicate the superior performance of our method that surpasses the previous approaches. For various liver diseases, our results demonstrate a high correlation between the staging of real and virtual stains (ρ=0.82; 95% CI: 0.73-0.89). In addition, our finetuning strategy is able to standardize the staining color and release the memory and computational burden, which can be employed in clinical assessment.

Kinetics of Shear-Induced Structural Ordering in Dense Colloids.

The macroscopic rheological response of a colloidal solution is highly correlated with the local microscopic structure, as revealed by an in situ Rheo-SAXS experiment with a high temporal resolution. Oscillatory shear can induce a strain-controlled ordering-to-disorder transition, resulting in a shear-thickening process that is different from the normal shear-thickening behavior that is driven by hydrodynamics and particle friction. We reveal that there is a complex time-dependent kinetics toward structural ordering under different applied strains. When the strain amplitude reaches a critical value that starts to induce disordering in the system, the pathway toward the dynamic equilibrium can also become highly non-monotonic. Within the same oscillatory cycle, there is a strong correlation of ordering with different phases of the oscillation, with the system oscillating between two dynamic metastable states.

Unsupervised domain adaptive tumor region recognition for Ki67 automated assisted quantification.

PURPOSE: Ki67 is a protein associated with tumor proliferation and metastasis in breast cancer and acts as an essential prognostic factor. Clinical work requires recognizing tumor regions on Ki67-stained whole-slide images (WSIs) before quantitation. Deep learning has the potential to provide assistance but largely relies on massive annotations and consumes a huge amount of time and energy. Hence, a novel tumor region recognition approach is proposed for more precise Ki67 quantification. METHODS: An unsupervised domain adaptive method is proposed, which combines adversarial and self-training. The model trained on labeled hematoxylin and eosin (H&E) data and unlabeled Ki67 data can recognize tumor regions in Ki67 WSIs. Based on the UDA method, a Ki67 automated assisted quantification system is developed, which contains foreground segmentation, tumor region recognition, cell counting, and WSI-level score calculation. RESULTS: The proposed UDA method achieves high performance in tumor region recognition and Ki67 quantification. The AUC reached 0.9915, 0.9352, and 0.9689 on the validation set and internal and external test sets, respectively, substantially exceeding baseline (0.9334, 0.9167, 0.9408) and rivaling the fully supervised method (0.9950, 0.9284, 0.9652). The evaluation of automated quantification on 148 WSIs illustrated statistical agreement with pathological reports. CONCLUSION: The model trained by the proposed method is capable of accurately recognizing Ki67 tumor regions. The proposed UDA method can be readily extended to other types of immunohistochemical staining images. The results of automated assisted quantification are accurate and interpretable to provide assistance to both junior and senior pathologists in their interpretation.

Stretching of immersed polyelectrolyte brushes in shear flow.

The way that polymer brushes respond to shear flow has important implications in various applications, including antifouling, corrosion protection, and stimuli-responsive materials. However, there is still much to learn about the behaviours and mechanisms that govern these responses. To address this gap in knowledge, our study uses in situ X-ray reflectivity to investigate how poly(styrene sulfonate) (PSS) brushes stretch and change in different environments, such as isopropanol (a poor solvent), water (a good solvent), and aqueous solutions containing various cations (Cs+, Ba2+, La3+, and Y3+). We have designed a custom apparatus that exposes the PSS brushes to both tangential shear forces from the primary flow and upward drag forces from a secondary flow. Our experimental findings clearly show that shear forces have a significant impact on how the chains in PSS brushes are arranged. At low shear rates, the tangential shear force causes the chains to tilt, leading to brush contraction. In contrast, higher shear rates generate an upward shear force that stretches and expands the chains. By analysing electron density profiles obtained from X-ray reflectivity, we gain valuable insights into how the PSS brushes respond structurally, especially the role of the diffuse layer in this dynamic behaviour. Our results highlight the importance of the initial chain configuration, which is influenced by the solvent and cations present, in shaping how polymer brushes respond to shear flow. The strength of the salt bridge network also plays a crucial role in determining how easily the brushes can stretch, with stronger networks offering more resistance to stretching. Ultimately, our study aims to enhance our understanding of polymer physics at interfaces, with a particular focus on practical applications involving polymer brushes.

Molecular subtypes and tumor microenvironment infiltration signatures based on cuproptosis-related genes in colon cancer.

Background: Colon cancer is one of the common cancers, and its prognosis remains to be improved. The role of cuproptosis as a newly discovered form of cell death in the development of colon cancer has not been determined. Methods: Based on 983 colon cancer samples in the TCGA database and the GEO database, we performed a comprehensive genomic analysis to explore the molecular subtypes mediated by cuproptosis-related genes. Single-sample gene set enrichment analysis (ssGSEA) was utilized to quantify the relative abundance of each cell infiltrate in the TME. A risk score was established using least absolute shrinkage and selection operator regression (LASSO), and its predictive ability for colon cancer patients was verified to explore its guiding value for treatment. Results: We identified two distinct cuproptosis-related molecular subtypes in colon cancer. These two distinct molecular subtypes can predict clinicopathological features, prognosis, TME activity, and immune-infiltrating cells. A risk model was developed and its predictive ability was verified. Compared with patients in the high-risk score group, patients in the low-risk score group were characterized by lower tumor microenvironment score, higher stem cell activity, lower tumor mutational burden, lower microsatellite instability, higher sensitivity to chemotherapeutics, and better immunotherapy efficacy. Conclusion: This study contributes to understanding the molecular characteristics of cuproptosis-related subtypes. We demonstrate a critical role for cuproptosis genes in colon cancer s in the TME. Our study contributes to the development of individualized treatment regimens for colon cancer.

Polymers in Lithium-Sulfur Batteries.

Lithium-sulfur batteries (LSBs) hold great promise as one of the next-generation power supplies for portable electronics and electric vehicles due to their ultrahigh energy density, cost effectiveness, and environmental benignity. However, their practical application has been impeded owing to the electronic insulation of sulfur and its intermediates, serious shuttle effect, large volume variation, and uncontrollable formation of lithium dendrites. Over the past decades, many pioneering strategies have been developed to address these issues via improving electrodes, electrolytes, separators and binders. Remarkably, polymers can be readily applied to all these aspects due to their structural designability, functional versatility, superior chemical stability and processability. Moreover, their lightweight and rich resource characteristics enable the production of LSBs with high-volume energy density at low cost. Surprisingly, there have been few reviews on development of polymers in LSBs. Herein, breakthroughs and future perspectives of emerging polymers in LSBs are scrutinized. Significant attention is centered on recent implementation of polymers in each component of LSBs with an emphasis on intrinsic mechanisms underlying their specific functions. The review offers a comprehensive overview of state-of-the-art polymers for LSBs, provides in-depth insights into addressing key challenges, and affords important resources for researchers working on electrochemical energy systems.

Sharpening Nanofiltration: Strategies for Enhanced Membrane Selectivity.

Nanofiltration plays an increasingly large role in many industrial applications, such as water treatment (e.g., desalination, water softening, and fluoride removal) and resource recovery (e.g., alkaline earth metals). Energy consumption and benefits of nanofiltration processes are directly determined by the selectivity of the nanofiltration membranes, which is largely governed by pore-size distribution and Donnan effects. During operation, the separation performance of unmodified nanofiltration membranes will also be impacted (deleteriously) upon unavoidable membrane fouling. Many efforts, therefore, have been directed toward enhancing the selectivity of nanofiltration membranes, which can be classified into membrane fabrication method improvement and process intensification. This review summarizes recent developments in the field and provides guidance for potential future approaches to improve the selectivity of nanofiltration membranes.

An in situ shearing x-ray measurement system for exploring structures and dynamics at the solid-liquid interface.

Revealing interfacial structure and dynamics has been one of the essential thematic topics in material science and condensed matter physics. Synchrotron-based x-ray scattering techniques can deliver unique and insightful probing of interfacial structures and dynamics, in particular, in reflection geometries with higher surface and interfacial sensitivity than transmission geometries. We demonstrate the design and implementation of an in situ shearing x-ray measurement system, equipped with both inline parallel-plate and cone-and-plate shearing setups and operated at the advanced photon source at Argonne National Laboratory, to investigate the structures and dynamics of end-tethered polymers at the solid-liquid interface. With a precise lifting motor, a micrometer-scale gap can be produced by aligning two surfaces of a rotating upper shaft and a lower sample substrate. A torsional shear flow forms in the gap and applies tangential shear forces on the sample surface. The technical combination with nanoscale rheology and the utilization of in situ x-ray scattering allow us to gain fundamental insights into the complex dynamics in soft interfaces under shearing. In this work, we demonstrate the technical scope and experimental capability of the in situ shearing x-ray system through the measurements of charged polymers at both flat and curved interfaces upon shearing. Through the in situ shearing x-ray scattering experiments integrated with theoretical simulations, we aim to develop a detailed understanding of the short-range molecular structure and mesoscale ionic aggregate morphology, as well as ion transport and dynamics in soft interfaces, thereby providing fundamental insight into a long-standing challenge in ionic polymer brushes with a significant technological impact.

[Experimental study on apoptosis of gastric cancer cell line MKN45 induced by continuous irradiation from iodine-125 seeds].

OBJECTIVE: To investigate the apoptotic effect of iodine-125 seeds on gastric cancer cell line MKN45 and its changes, including the changes of cellular morphosis, early apoptotic rate and Bcl-2/Bax expression, and to provide further evidence for the clinical application of iodine-125 seeds on gastric cancer. METHODS: Human gastric cancer cell line MKN45 in treatment group was continuously exposed to discoid radiative sources made by seven iodine-125 seeds, while the control group not treated. 72 hours later, the gastric cancer cells in each group were collected, and observed respectively under light and electron microscope. Apoptotic rate were detected with DAPI staining. Early apoptotic rate was detected by flow cytometry (FCM). And Bcl-2/Bax expression was detected by RT-PCR. RESULTS: 1) Typical morphologic changes of apoptotic cells were observed under light and electronic microscope in treatment group. 2) Apoptotic rate detected with DAPI staining in treatment group was higher than that in control group (p<0.01). 3) Early apoptotic rate in treatment group was higher than that in control group (P<0.01). 4) Increased intracellular Bax expression level detected by RT-PCR in treatment group was observed (P<0.01). 5) Bax expression level in treatment group was increased obviously, with the decrese of Bcl-2 expression by semi-quantitative RT-PCR detection. The ratio of Bcl-2 to Bax in treatment group was lower than that in control group. CONCLUSION: Continous irradiation by iodine-125 seeds can induce apoptosis of human gastric cancer cell line MKN45. The decreasing ratio of Bcl-2 to Bax may be one of the mechanism of human gastric cancer cell line MKN45's apoptosis.

Direct Observation of Structural Deformation Immunity for Understanding Oxygen Plasma Treatment-Enhanced Resistive Switching in HfOx-Based Memristive Devices.

Oxygen ions' migration is the fundamental resistive switching (RS) mechanism of the binary metal oxides-based memristive devices, and recent studies have found that the RS performance can be enhanced through appropriate oxygen plasma treatment (OPT). However, the lack of experimental evidence observed directly from the microscopic level of materials and applicable understanding of how OPT improves the RS properties will cause significant difficulties in its further application. In this work, we apply scanning probe microscope (SPM)-based techniques to study the OPT-enhanced RS performance in prototypical HfOx based memristive devices through in situ morphology and electrical measurements. It is first found that the structural deformations in HfOx nanofilm induced by migration of oxygen ions and interfacial electrochemical reactions can be recovered by OPT effectively. More importantly, such structural deformations no longer occur after OPT due to the strengthening in lattice structure, which directly illustrates the enhanced quantity of HfOx nanofilm and the nature of enhanced RS properties after OPT. Finally, the underlying mechanisms of OPT-enhanced RS performance are analyzed by the results of X-ray photoelectron spectroscopic (XPS) surface analysis. In the OPT-enhanced HfOx nanofilm, oxygen vacancies in crystalline regions can be remarkably reduced by active oxygen ions' implantation. The oxygen ions transport will depend considerably on the grain boundaries and OPT-enhanced lattice structure will further guarantee the stability of conductive filaments, both of which ensure the uniformity and repeatability in RS processes. This study could provide a scientific basis for improving RS performance of oxides-based memristive devices by utilizing OPT.

Following the Morphological Disruption by an Electrolyte of a Buried Interface.

A challenge of broad interest in both materials science and biology is the study of interfaces that are buried within a structure, particularly multilayer structures. Despite the enormous costs of corrosion and many decades of corrosion research, details of the mechanisms of various sorts of corrosion are still not clear, in part due to the difficulty in interrogating the interface between the corroding metal and an organic coating, which is typically used to mitigate corrosion. Generally, the performance of such coatings is evaluated by visual inspection after exposure or by modeling impedance data, which is a process not straightforwardly connected to physical interface structures. "Rocking-curve" X-ray scattering measurements provide a means of probing such interfaces due to the ability of X-rays to penetrate materials. Here, variations in the morphology of an interface between a protective coating and a metal substrate due to exposure to an electrolyte are derived from analysis of rocking-curve data in conjunction with atomic force microscopy imaging of the outer coating surface. The interfaces of cross-linked epoxy coatings with aluminum are irreversibly changed after 12 h of contact between the electrolyte solution and the face of the coating. The character of this change varies with the molecule used to cross-link the coating. Since X-ray off-specular scattering is sensitive to changes on the nanometer scale, it is also able to register interface degradation on time scales shorter than those probed by many other techniques, potentially expediting the evaluation of coatings for protection against degradation of the interface.