艾司摩尔

We report a systematic experimental study of the resistance of thin film metal oxides (MOx) to chemical reduction by atomic hydrogen (H*) at 700 degrees C. Thin films of Y2O3, HfO2, ZrO2, TiO2, Nb2O5 and Al2O3 are selected based on their relevance to various coating applications in, for instance, integrated circuits and extreme ultraviolet lithography (EUVL) scanners. In the study 15-20 nm thick MOx thin films were thermally annealed at 900 degrees C and thus stabilized before exposure to H* at 700 degrees C. Comprehensive characterization using X-ray photoelectron spectroscopy, X-ray reflectivity, X-ray diffraction, atomic force microscopy, and in-situ ellipsometry revealed two distinct categories of behavior, which are combined with equilibrium thermodynamics assessments. Upon exposure to H*, Y2O3, HfO2, ZrO2, and Al2O3 exhibited high resistance to reduction, with minor to no morphological, structural or compositional changes, consistent with thermodynamic predictions. In contrast, TiO2 and Nb2O5 underwent phase transformation and reduction to lower oxidation states. At present systematic reports in the literature of MOx interaction and their reducibility by H* are scarce. We here provide insight into general trends of MOx stability that is relevant for the need of chemically stable coatings in reactive environments like H*, but also low-ion energy H plasmas that reside in EUVL scanners.

This work uses in situ ellipsometry to explore the kinetics of atomic layer deposition (ALD) of thin yttrium oxide (Y 2 O 3 ) films from 200 to 350 °C to find the process window using tris(methylcyclopentadienyl)yttrium ((MeCp) 3 Y) and water (H 2 O) precursors. The results show that an accurate control of H 2 O dosage with sufficient H 2 O purge is essential. To verify the occurrence of ALD, particular attention is paid to the film thickness (non‐)uniformity corresponding to saturation conditions of the growth‐per‐cycle (GPC) curves. A decrease in thickness nonuniformity from 63% (350 °C) down to <5% (200 °C) is observed, emphasizing the need to carefully select the process temperature ( T ) of Y 2 O 3 within the published ALD temperature window (200–400 °C). This work demonstrates that purely relying on the GPC saturation curves does not necessarily ensure the occurrence of ALD and highlights the importance of combining the results of both growth kinetics and film thickness uniformity studies. For films deposited at T ≤ 250 °C, X‐ray photoelectron spectroscopy results show (i) no carbon impurity, (ii) limited hydroxylation, and (iii) an oxygen‐to‐yttrium ratio of ~1.14 after applying argon pre‐sputtering (with excess removal of oxygen during the sputtering). The ~10 nm Y 2 O 3‐x films exhibit a polycrystalline structure as confirmed by X‐ray diffractometry.

Polyatomic kinetic models are essential for accurately capturing the thermodynamic behavior of real gases, as internal energy modes significantly influence transport coefficients, relaxation processes, and non-equilibrium effects that cannot be represented by monoatomic models. The polyatomic ESBGK model describes molecular collisions as a relaxation towards a generalized Gaussian distribution with an anisotropic covariance matrix and an exponentially decaying internal energy distribution. We present a new semi-Lagrangian scheme for the polyatomic Ellipsoidal Statistical BGK (ESBGK) model of the Boltzmann equation. The semi-Lagrangian framework, being deterministic and grid-based, removes the time-step restriction associated with the linear transport term by following the method of characteristics. The potentially stiff relaxation term is treated using an implicit A-stable linear multistep method which, owing to the structure of the BGK operator, can be reformulated into a cheap time-stepping scheme. This yields a highly efficient and numerically stable method. The numerical method is asymptotic preserving and stiffly accurate, meaning the scheme asymptotically converges to a scheme for the Euler equations in the vanishing Knudsen limit. In addition, we prove that the first-order scheme, asymptotically converges to the compressible Navier-Stokes equation with correct transport coefficients. Finally, we propose inflow and outflow boundary conditions suitable for BGK-type kinetic equations. We perform simulations of the Fourier and Couette test case to compare the BGK model with Direct Simulation Monte Carlo (DSMC). To conclude, we demonstrate the method on a challenging orifice flow test case with moving boundaries.

The agile way of working (AWOW) is increasingly adopted as a flexible management approach that enables organizational responsiveness, particularly in dynamic and uncertain environments. Yet the psychological mechanisms underlying its effectiveness remain insufficiently understood. Drawing on a time-lagged cross-sectional survey design, this study examines the relationships between the AWOW, adaptivity, and work engagement among agile team members. Psychological empowerment is proposed as a mediating mechanism, while product owner support, reflected in behavior such as clarifying goals and prioritizing tasks, is examined as a substitute for the AWOW. Results indicate that the AWOW is positively associated with both adaptivity and work engagement, with psychological empowerment partially mediating these relationships. A substitutive interaction effect is observed: the positive association between the AWOW and psychological empowerment is stronger under conditions of lower product owner support. These findings contribute to understanding the social behavioral dynamics within agile teams by linking the AWOW to adaptivity and work engagement, extend empowerment theory by testing its core mechanisms in a highly dynamic AWOW context, and clarify the role of product owner supportive behavior as a leadership (product owner) process in agile settings.

Cloud-based insurance systems face cumulative challenges from cyber vulnerabilities, regulatory non-compliance, and active threat landscapes that cooperate data integrity and operational resilience. Established security and risk evaluation methods lack adaptive intelligence and automation to manage such increasing risks effectively. This research proposes an Adaptive Security Framework for AI-Driven Risk Assessment and Compliance Automation in Cloud Security and Vulnerability Administration within the Insurance Domain. The proposed method integrates TensorFlow Extended (TFX) for computerized ML orchestration, TensorFlow for deep learning-based risk prediction, and TF-Agents for adaptive reinforcement learning to progress real-time response and compliance adherence. This outline integrates PCA-fueled feature extraction, Z-score normalization, and reinforcement characteristic coverage to resource available, privacy-preserving emulators of data-driven persistence. Experimental calculation using the Cyber-attack insurance dataset Shows higher performance than standard models like Random Forest, XGBoost and CNN-LSTM with Metrics of (Accuracy 0.992; Precision 0.987; Recall 0.989, F1 Score 0.988; AUC-ROC 0.995) The proposed design ensures automated and real-time compliance management for better-insured cyber security infrastructures.