Results 201 to 210 of about 7,125 (291)
A Pressure Microsensor Made of Parylene‐C for Use as Medical Implant
A monolithic parylene‐C pressure sensor with gold strain gauges provides 6.2 μV$\mu{\rm V}$·mmHg$\cdot{\rm mmHg}$−1$^{-1}$ sensitivity. The morphology of a sputtered thin film strain sensor is granular/columnar, which results in a high gauge factor of 7.5. Thermal bonding and parylene‐C coating create a hermetic cavity.
Ann‐Kathrin Klein +2 more
wiley +1 more source
The nonlinear trajectory of post-stroke aphasia recovery. [PDF]
Maraka JO +6 more
europepmc +1 more source
A MEMS‐integrated metamaterial filter enables continuous, low‐voltage spectral tuning in the long‐wavelength infrared (LWIR). The device employs extraordinary optical transmission in a dual suspended metasurface stack, where electrostatic actuation precisely controls the intermembrane air gap.
Oleg Bannik +6 more
wiley +1 more source
Creep behavior of clayey soil and its model prediction in the Cangzhou land subsidence area. [PDF]
Qi J, Xie Y, Li C, Guo H, Wang Y.
europepmc +1 more source
Reconnaissance Peptide Labeling Grain Boundary of Chemically Grown MoS2 Polycrystalline Monolayer
Self‐assembled peptides on substrates, through adsorption and aggregation, offer an alternative way to label grain boundaries in chemically grown single‐layer polycrystalline MoS2. During an early nucleation step, peptides preferentially bind to grain boundaries.
Linhao Sun, Jinhua Hu
wiley +1 more source
Machine learning-based prognostic modeling for locally advanced non-small cell lung cancer treated with immuno-radiotherapy. [PDF]
Lu S +5 more
europepmc +1 more source
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov +3 more
wiley +1 more source
Advanced Production, Processing and Characterization of Industrial Materials. [PDF]
Mascenik J, Krenicky T.
europepmc +1 more source

