High-pressure hydrogen storage systems (up to 100 MPa) require advanced materials for safety and reliability, particularly for rubber seals, which are prone to degradation under extreme conditions. This study explores the use of cellulose nanofibers (CNFs) and cellulose nanocrystals (CNCs) as sustainable nanofillers for nitrile butadiene rubber (NBR) latex nanocomposites. CNFs and CNCs were evaluated for their effects on mechanical properties, crosslink density, and resistance to high-pressure hydrogen exposure in rubber O-rings. A life cycle assessment (LCA) was carried to assess the environmental impact of these nanocomposites. The results showed that CNF-filled NBR composites exhibited better resistance to bubble formation under hydrogen exposure compared to CNC-filled ones. However, the LCA indicated relatively high environmental impacts for both, reaching up to 2.5 kg CO₂ eq. in Global Warming Potential (GWP), highlighting the need for further optimization of production processes. This study demonstrates the potential of cellulose nanofillers to improve NBR performance while advancing sustainable hydrogen storage materials.

Rubber materials undergo continuous wear in high-pressure seal applications. To address the risk of adhesive wear and consequent leakage of rubber seals operating under reciprocating sliding in high-pressure hydrogen storage and refueling systems, this study employed high-pressure hydrogen tribology testing. Ball-on-disk reciprocating tests were conducted using a 316L stainless-steel ball against silica-filled nitrile butadiene rubber (NBR), and the friction response and wear-morphology evolution were compared under ambient air, 1 MPa hydrogen (H₂), 50 MPa H₂, 50 MPa nitrogen (N₂), and grease-coated conditions. Under dry sliding, the coefficient of friction (COF) of NBR in air and hydrogen ranged from 1.34 to 1.44, whereas it decreased markedly to 0.942 in 50 MPa N₂. The wear volume under the four dry conditions was concentrated in the range of ~0.292–0.320 mm³. After grease coating, the steady-state COF in air and at 50 MPa H₂ dropped to 0.099 and 0.105, respectively, and the wear features changed from ridge-like wear patterns/tear pits to regular, smooth indentations with slight running marks. The results demonstrate that a lubricating film can effectively separate direct metal–rubber contact and suppress stick–slip, enabling a low-friction, low-wear, and highly stable interface in high-pressure hydrogen, and providing a practical engineering route for reliable operation of rubber seals in hydrogen service.

Background: Accurate preoperative prediction of lymph node metastasis (LNM) is essential for tailoring treatment strategies in T1 colorectal cancer (CRC). Although the neutrophil-to-lymphocyte ratio (NLR), an easily acquired inflammatory biomarker, correlates with tumor progression, its incremental value when incorporated into machine learning (ML) models for predicting LNM in T1 CRC remains unclear. Therefore, this study evaluated the independent predictive value of preoperative NLR for LNM and constructed interpretative ML models to stratify LNM risk in T1 CRC. Methods: We retrospectively enrolled 533 pathologically confirmed T1 CRC patients. NLR was calculated as the neutrophil–lymphocyte count ratio. Multivariable logistic regression identified NLR’s independent correlation with LNM. Features were screened via combined least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm. Six ML models, including Logistic Regression (LR), Random Forest (RF), XGBoost, LightGBM, CatBoost, and Logistic Regression with Splines (LR-Spline), were developed and validated. Model performance was evaluated in terms of discrimination, calibration, and clinical utility. Shapley additive explanations (SHAP) were applied for feature interpretability. Results: Elevated preoperative NLR was independently associated with an increased risk of LNM (adjusted OR = 2.83, 95% CI: 1.48–5.41, p = 0.002), exhibiting stability across all clinical and pathological subgroups. The optimized LightGBM outperformed other models with an area under the receiver operating characteristic curve (AUC) of 0.768, alongside favorable clinical utility. Conclusions: An elevated preoperative NLR is a robust, independent predictor of LNM in patients with T1 CRC. An optimized LightGBM model integrating NLR with routine clinicopathological indicators offers accurate risk stratification, potentially refining surgical decision-making and sparing low-risk T1 CRC patients from unnecessary radical resections.