Cellulose nanocrystals intercalated clay biocomposite for rapid Cr(VI) removal.

The application of bentonite (Bt) as an adsorbent for heavy metals has been limited due to its hydrophobicity and insufficient surface area. Herein, we present cellulose nanocrystal (CNC) modified Bt composite (CNC@Bt) with enhanced efficiency for Cr(VI) removal. CNC@Bt exhibited an increased specific surface area and a porous structure, while maintaining the original crystal structure of Bt. This was achieved through a synergistic function of ion exchange, hydrogen bonding, electrostatic interactions, and steric hindrance. The adsorption of Cr(VI) by CNC@Bt followed the pseudo-second-order kinetic and Langmuir isotherm adsorption model. Moreover, the process was endothermic and spontaneous. At an initial Cr(VI) concentration of 20 mg/L and pH = 4.0, 10 g/L CNC@Bt achieved a removal rate of 92.7%, and the adsorption capacity was 1.85 mg/g, significantly higher than bare Bt (37.9% and 0.76 mg/g). The removal efficiency remained consistently above 80% over a wide pH range, indicating the potential practical applicability of CNC@Bt. With its fast adsorption rate, pH adaptability, and stable performance, CNC@Bt presents promising prospects for the rapid treatment of Cr-contaminated wastewater.

Research progress of novel bio-denitrification technology in deep wastewater treatment.

Excessive nitrogen emissions are a major contributor to water pollution, posing a threat not only to the environment but also to human health. Therefore, achieving deep denitrification of wastewater is of significant importance. Traditional biological denitrification methods have some drawbacks, including long processing times, substantial land requirements, high energy consumption, and high investment and operational costs. In contrast, the novel bio-denitrification technology reduces the traditional processing time and lowers operational and maintenance costs while improving denitrification efficiency. This technology falls within the category of environmentally friendly, low-energy deep denitrification methods. This paper introduces several innovative bio-denitrification technologies and their combinations, conducts a comparative analysis of their denitrification efficiency across various wastewater types, and concludes by outlining the future prospects for the development of these novel bio-denitrification technologies.

A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware.

Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders their deployment on mobile and embedded systems. Highly inspired from biological brain, spiking neural networks (SNNs) are emerging as new solutions because of natural superiority in brain-like learning and great energy efficiency with event-driven communication and computation. Nevertheless, training a deep SNN remains a main challenge and there is usually a big accuracy gap between ANNs and SNNs. In this paper, we introduce a hardware-friendly conversion algorithm called "scatter-and-gather" to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary {-1,0,1} synaptic weights. Each spiking neuron is stateless and more like original McCulloch and Pitts model, because it fires at most one spike and need be reset at each time step. Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. Experimental results show our spiking LeNet on MNIST and VGG-Net on CIFAR-10 datasetobtain 99.37% and 91.91% classification accuracy, respectively. Besides, the presented mapping algorithm manages network deployment on our neuromorphic chip with maximum resource efficiency and excellent flexibility. Our four-spike LeNet and VGG-Net on chip can achieve respective real-time inference speed of 0.38 ms/image, 3.24 ms/image, and an average power consumption of 0.28 mJ/image and 2.3 mJ/image at 0.9 V, 252 MHz, which is nearly two orders of magnitude more efficient than traditional GPUs.

Removal of Pb(II) from aqueous solutions by adsorption on magnetic bentonite.

Bentonite is a porous clay material that shows good performance for adsorbing heavy metals and other pollutants for wastewater remediation. In our previous study, magnetic bentonite (M-B) was prepared to solve the separation problem and improve the operability. In this study, we investigated the influence of various parameters on the Pb(II) adsorption of M-B, and it showed effective performance. About 98.9% adsorption removal rate was achieved within 90 min at adsorbent dose of 10 g/L for initial Pb(II) concentration of 200 mg/L at 40 °C and pH 5. The adsorption kinetic fit well by the pseudo-second-order model, and also followed the intra-particle diffusion model up to 90 min. Moreover, adsorption data were successfully reproduced by the Langmuir isotherm; the maximum adsorption capacity was calculated as 80.40 mg/g. The mechanism of interaction between Pb(II) ions and M-B was ionic exchange, surface complexation, and electro-static interactions. Thermodynamics study indicated that the reaction of Pb(II) adsorption on M-B was endothermic and spontaneous; increasing the temperature promoted adsorption. This study was expected to provide a reference and theoretical basis for the treatment of Pb-containing wastewater using bentonite materials.

Adsorption behavior of magnetic bentonite for removing Hg(ii) from aqueous solutions.

Bentonite is a porous clay material that shows good performance for adsorbing heavy metals and other pollutants for wastewater remediation. However, it is very difficult to separate the bentonite from water after adsorption as it forms a stable suspension. In this paper, we prepared magnetic bentonite (M-B) by loading Fe3O4 particles onto aluminum-pillared bentonite (Al-B) in order to facilitate its removal from water. The functional groups, skeleton structure, surface morphology and electrical changes of the prepared material were investigated by FT-IR, XRD, BET, SEM, VSM and zeta potential measurements. It was used as an adsorbent for Hg(ii) removal from aqueous solutions and the influence of various parameters on the adsorption performance was investigated. The adsorption kinetics were best fitted by the pseudo-second-order model, and also followed the intra-particle diffusion model up to 18 min. Moreover, adsorption data were successfully reproduced by the Langmuir isotherm, and the Hg(ii) adsorption saturation capacity was determined as 26.18 mg g-1. The average adsorption free energy change calculated by the D-R adsorption isotherm model was 11.89 kJ mol-1, which indicated the occurrence of ionic exchange. The adsorption thermodynamic parameter ΔH was calculated as 42.92 kJ mol-1, which indicated chemical adsorption. Overall, the thermodynamic parameters implied that Hg(ii) adsorption was endothermic and spontaneous.