The diaphragm vacuum pump has a simple structure, reliable sealing, and oil-free operation, which is crucial for clean vacuum systems. However, its application is limited by the diaphragm fatigue life and achievable ultimate pressure. Corrugated diaphragms help reduce structural stress and improve fatigue life. However, their design theories and design methods have not been found or reported. This study proposes applying the equivalent stiffness theory to diaphragm morphology design. The results show that a diaphragm with lower equivalent stiffness has reduced structural stress and improved fatigue life. The effects of the amplitude, number, and projected length of corrugations on pump performance are simulated using the finite element method with ABAQUS software. The optimal ranges of diaphragm dimensionless parameters are proposed. A gradient corrugated structure is proposed to resolve the conflict between stress value and ultimate pressure. Finally, a design method for the corrugated diaphragm morphology based on the equivalent stiffness theory is proposed. This study provides a theory and a design method for the corrugated diaphragm, which is significant for diaphragm vacuum pumps.
We report the first superconducting thin film phase change material that has both a crystallization temperature above room temperature and makes a transition into a superconducting state within a temperature range measured here to be 0.5 +/- 0.2 K. This opens the prospect for a latching microwave switch with an infinite cut-off frequency. The phase change material used is a mixture of SnTe and GeTe, which when a heat pulse is applied transforms between amorphous and crystalline states, latching into either the crystalline on-state or the amorphous off-state, depending upon the magnitude and duration of the pulse, and is reversible with the appropriate following pulse.
In this study, we combine a kinetic Monte Carlo model of submonolayer growth with scanning-probe-inspired localized postgrowth perturbations to examine morphology control after deposition. This approach offers a route to reshape an already grown surface, but selecting an effective protocol is difficult because improving one morphology feature can degrade another. We define each perturbation protocol by the active scanned fraction, perturbation strength, scan speed, and number of scan repetitions. Bayesian optimization is used as an initial search tool to identify protocols that modify island morphology while limiting undesirable changes in island density, roughness, mean island size, and island-size dispersion. The morphology score rewards controlled changes in island aspect ratio while penalizing increases in island density, roughness, size dispersion, and excessive anisotropy. On the 100 & times; 100 lattice, the search and independent larger-ensemble confirmation calculations both identify 90% active scanning and a barrier reduction of 0.60 eV as the leading tested condition. Calculations using lateral lattice sizes of 100, 120, and 150 sites nevertheless show that the ranking of strong perturbations is size sensitive, so this condition is not treated as universally optimal. Pass-number calculations on the largest lattice reveal a clear perturbation-dose effect: additional scan passes reduce the overall morphology score and increase island-size dispersion. Collectively, these results show that localized postgrowth perturbations can guide morphology modification in nonequilibrium surface growth, but effective morphology control requires finite and selective intervention rather than prolonged or repeated exposure.
In photomask manufacturing, process-induced contour errors after electron-beam writing and downstream processing can limit pattern fidelity even when the target layout is geometrically well defined. Mask process correction (MPC) precompensates the written layout, but conventional model-based MPC can require repeated process evaluation and local geometry updates for dense, contour-rich layouts. This study presents CAUNet (Contour-Aware U-Net)-MPC, a learning-assisted workflow that combines supervised process identification and reference-mask initialization with self-supervised process-consistency optimization. Under one fixed industrial electron-beam lithography condition, contour-aware U-Nets are trained from coordinate-aligned target patterns and measured contours and then fixed as differentiable process surrogates. An inverse CAUNet initialized from reference MPC masks is subsequently trained by a cross-surrogate minimax objective over two heterogeneous frozen process models. On a guarded spatial test split, three process surrogates achieved mean intersection-over-union (IoU) values of 0.9895-0.9899 and mean tile EPE 95 values of 1.937-1.981 nm against measured contours. Under P A, CAUNet-MPC reduced mean tile EPE 95 from 33.288 nm for No MPC, 13.184 nm for the actual industrial reference masks, and 13.056 nm for the locked warm-start network to 1.862 +/- 0.062 nm across three single-network correction chains, while increasing IoU to 0.995 552 +/- 0.000 222. The second optimization surrogate and a held-out architecture-transfer surrogate yielded 1.866 +/- 0.205 and 1.877 +/- 0.088 nm, respectively. These results establish CAUNet-MPC as a robust self-supervised route for fixed-process MPC candidate generation.
To address persistent challenges in molecular detection-particularly signal variability, low analyte abundance, and poor spectral reproducibility-a portable bead-stacked nanosieve platform was engineered to enable robust on-chip Raman signal amplification using silver nanocubes (AgNCs) functionalized with Raman-active thiolated ligands. The microdevice integrates deformable elastomeric channels with magnetic bead stacking to immobilize and concentrate AgNCs within confined nanoscopic junctions, thereby generating densely packed plasmonic hot spot architectures that markedly enhance surface-enhanced Raman scattering efficiency. Time-resolved measurements revealed substantial increases in the Raman intensity for both 4-mercaptobenzoic acid (4-MBA) and 4-aminothiophenol (4-ATP), outperforming corresponding off-chip assays and maintaining stable enhancement factors over a 4 h operational window. Furthermore, the platform successfully demonstrated multiplexed sensing capability: mixed-analyte experiments containing 4-MBA, 4-ATP, and cysteamine produced clearly distinguishable spectral fingerprints, even under serial dilution and dynamically pulsed sample delivery. These results establish the bead-stacked nanosieve as a versatile and scalable diagnostic architecture capable of achieving high sensitivity and spectral fidelity under continuous-flow conditions.
