Insight into the mechanism of nano-TiO2-doped biochar in mitigating cadmium mobility in soil-pak choi system.

Soil cadmium (Cd) pollution poses severe threats to food security and human health. Previous studies have reported that both nanoparticles (NPs) and biochar have potential for soil Cd remediation. In this study, a composite material (BN) was synthesized using low-dose TiO2 NPs and silkworm excrement-based biochar, and the mechanism of its effect on the Cd-contaminated soil-pak choi system was investigated. The application of 0.5 % BN to the soil effectively reduced 24.8 % of diethylenetriaminepentaacetic acid (DTPA) Cd in the soil and promoted the conversion of Cd from leaching and HOAc-extractive to reducible forms. BN could improve the adsorption capacity of soil for Cd by promoting the formation of humic acid (HA) and increasing the cation exchange capacity (CEC), as well as activating the oxygen-containing functional groups such as CO and CO. BN also increased soil urease and catalase activities and improved the synergistic network among soil bacterial communities to promote soil microbial carbon (C) and nitrogen (N) cycling, thus enhancing Cd passivation. Moreover, BN increased soil biological activity-associated metabolites like T-2 Triol and altered lipid metabolism-related fatty acids, especially hexadecanoic acid and dodecanoic acid, crucial for bacterial Cd tolerance. In addition, BN inhibited Cd uptake and root-to-shoot translocation in pak choi, which ultimately decreased Cd accumulation in shoots by 51.0 %. BN significantly increased the phosphorus (P) uptake in shoots by 59.4 % by improving the soil microbial P cycling. This may serve as a beneficial strategy for pak choi to counteract Cd toxicity. These findings provide new insights into nanomaterial-doped biochar for remediation of heavy metal contamination in soil-plant systems.

Near-Field Beamforming Algorithms for UAVs.

This study presents three distributed beamforming algorithms to address the challenges of positioning and signal phase errors in unmanned aerial vehicle (UAV) arrays that hinder effective beamforming. Firstly, the array's received signal phase error model was analyzed under near-field conditions. In the absence of navigation data, a beamforming algorithm based on the Extended Kalman Filter (EKF) was proposed. In cases where navigation data were available, Taylor expansion was utilized to simplify the model, the non-Gaussian noise of the compensated received signal phase was approximated to Gaussian noise, and the noise covariance matrix in the Kalman Filter (KF) was estimated. Then, a beamforming algorithm based on KF was developed. To further estimate the Gaussian noise distribution of the received signal phase, the noise covariance matrix was iteratively estimated using unscented transformation (UT), and here, a beamforming algorithm based on the Unscented Kalman Filter (UKF) was proposed. The proposed algorithms were validated through simulations, illustrating their ability to suppress the malign effects of errors on near-field UAV array beamforming. This study provides a reference for the implementation of UAV array beamforming under varying conditions.

Seismic ocean thermometry.

More than 90% of the energy trapped on Earth by increasingly abundant greenhouse gases is absorbed by the ocean. Monitoring the resulting ocean warming remains a challenging sampling problem. To complement existing point measurements, we introduce a method that infers basin-scale deep-ocean temperature changes from the travel times of sound waves that are generated by repeating earthquakes. A first implementation of this seismic ocean thermometry constrains temperature anomalies averaged across a 3000-kilometer-long section in the equatorial East Indian Ocean with a standard error of 0.0060 kelvin. Between 2005 and 2016, we find temperature fluctuations on time scales of 12 months, 6 months, and ~10 days, and we infer a decadal warming trend that substantially exceeds previous estimates.