As the leading cause of death, heart attacks result in millions of deaths annually, with no end in sight. Early intervention is the only strategy for rescuing lives threatened by heart disease. However, the detection time of the fastest heart‐attack detection system is >15 min, which is too long considering the rapid passage of life. In this study, a machine learning (ML)‐driven system with a simple process, low‐cost, short detection time (only 10 s), and high precision is developed. By utilizing a functionalized nanofinger structure, even a trace amount of biomarker leaked before a heart attack can be captured. Additionally, enhanced Raman profiles are constructed for predictive analytics. Five ML models are developed to harness the useful characteristics of each Raman spectrum and provide early warnings of heart attacks with >98% accuracy. Through the strategic combination of nanofingers and ML algorithms, the proposed warning system accurately provides alerts on silent heart‐attack attempts seconds ahead of actual attacks.

No abstract

Memristive devices are promising candidates for analog computing applications such as neuromorphic computation. Larger dynamic ranges and more sufficient multilevel states can enable the significant development of memristor‐based utilizations. Herein, a method to improve the analog switching performance of memristors through a hybrid tuning (coarse and fine tuning) of two sub‐filaments is demonstrated. The creation of sub‐filaments inside the dielectric switching layer is realized by deploying Pt metal islands in the switching layer. Given the different material stack configurations of the two sub‐filaments, they exhibit different switching properties to play the roles of coarse and fine tuning respectively in the memristor. Based on the above mechanism, a Pt/Ta/Al2O3/Pt island/Al2O3‐x/TiOy/Al2O3‐x/Pt memristor is proposed and fabricated. Through the hybrid tuning of two sub‐filaments, a combined dynamic range of 600 Ω to 50 kΩ is achieved. Compared to the reference Pt/Ta/Al2O3/Pt memristors (dynamic range: 600 Ω to 8 kΩ), both dynamic range and multilevel resistance states are increased significantly. Meanwhile, the energy efficiency is improved because the resistance of tunable states can be set to larger values. Furthermore, this mechanism can be incorporated into various existing memristors to improve their dynamic range and multilevel states, which extensively enriches the applications of memristors.

No abstract

The carbon dioxide reduction reaction (CO2RR) is a promising method to both reduce greenhouse gas carbon dioxide (CO2) concentrations and provide an alternative to fossil fuel by converting water and CO2 into high-energy-density chemicals. Nevertheless, the CO2RR suffers from high chemical reaction barriers and low selectivity. Here we demonstrate that 4 nm gap plasmonic nano-finger arrays provide a reliable and repeatable plasmon-resonant photocatalyst for multiple-electrons reactions: the CO2RR to generate higher-order hydrocarbons. Electromagnetics simulation shows that hot spots with 10,000 light intensity enhancement can be achieved using nano-gap fingers under a resonant wavelength of 638 nm. From cryogenic 1H-NMR spectra, formic acid and acetic acid productions are observed with a nano-fingers array sample. After 1 h laser irradiation, we only observe the generation of formic acid in the liquid solution. While increasing the laser irradiation period, we observe both formic and acetic acid in the liquid solution. We also observe that laser irradiation at different wavelengths significantly affected the generation of formic acid and acetic acid. The ratio, 2.29, of the product concentration generated at the resonant wavelength 638 nm and the non-resonant wavelength 405 nm is close to the ratio, 4.93, of the generated hot electrons inside the TiO2 layer at different wavelengths from the electromagnetics simulation. This shows that product generation is related to the strength of localized electric fields.