Effectively adsorb Au(S2O3)23- using aminoguanidine as trapping group from thiosulfate solutions.

Thiosulfate gold leaching is one of the most promising green cyanide-free gold extraction processes; however, the difficulty of recovering Au(I) from the leaching system hinders its further development. This study prepared aminoguanidine-functionalized microspheres (AGMs) via a one-step reaction involving nucleophilic substitution between aminoguanidine hydrochloride and chloromethylated polystyrene microspheres and used AGMs to adsorb Au(I) from thiosulfate solutions. Scanning electron microscopy, Brunauer-Emmett-Teller analysis, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy were used to analyze the structure and properties of AGMs. Experiments were designed to investigate the effects of pH, temperature, initial Au(I), and thiosulfate concentrations on the gold adsorption performance of AGMs. Results demonstrated that AGMs can efficiently adsorb Au(I) from thiosulfate solutions in a wide pH range. The adsorption process conforms to the pseudo-second-order kinetic model and Langmuir isotherm model, with a maximum capacity of 22.03 kg/t. Acidic thiourea is an effective desorbent, and after four adsorption-desorption cycles, the adsorption rate of Au(I) by AGMs is 78.63%, which shows AGMs have good cyclic application potential. Based on the results of characterization, experiments, and density functional theory calculations, the mechanism for the adsorption of [Au(S2O3)2]3- on AGMs involves anion exchange. Importantly, AGMs exhibited satisfactory adsorption property for Au(I) in practical Cu2+-NH3(en)-S2O32- systems. This study provided experimental reference for the recovery of Au(I) from thiosulfate solution.

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption is proposed for continuous cardiac monitoring applications. The detector is featured with a 1.5-bit non-feedback delta quantizer (DQ) based feature extractor, followed by a multiplier-less convolutional neural network (CNN) engine, which eliminates the traditional high-resolution analog-to-digital converter (ADC) in conventional signal processing systems. The DQ uses a computing-in-capacitor (CIC) subtractor to quantize the sample-to-sample difference of ECG signal into 1.5-bit ternary codes, which is insensitive to low-frequency baseline wandering. The subsequent event-driven classifier is composed of a low-complexity coarse detector and a systolic-array-based CNN engine for ECG anomaly detection. The DQ and the digital CNN are fabricated in 65-nm and 180-nm CMOS technology, respectively, and the two chips are integrated on board through wire bonding. The measured detection accuracy is 90.6% ∼ 91.3% when tested on the MIT-BIH arrhythmia database, identifying three different ECG anomalies. Operating at 1 V and 1.4 V power supplies for the DQ and the digital CNN, respectively, the measured long-term average power consumption of the core circuits is 36 nW, which makes the detector among those state-of-the-art always-on cardiac anomaly detection devices with the lowest power consumption.