Saline wastewater can cause severe damage to the natural environment, yet its physicochemical treatment methods are generally energy-intensive and costly. In this study, a novel straw foam-based aerobic granular sludge (SF-AGS) was employed to protect microbial communities from salinity stress under three experimental conditions: no salinity (R1), salinity with NaCl (R2), and salinity with NaCl + Na2SO4 (R3). Results indicated that mature SF-AGS maintained excellent settling performance and high biomass concentration even at 4.0% salinity. The SF-AGS exhibited high removal efficiencies for COD, NH4+-N, TP and TN in both pure-salt and mixed-salt reactors, achieving approximately 91%, 80%, 30% and 75%, respectively, highlighting its high tolerance to high-salinity conditions without significant deterioration in overall performance. Gradual salinity increases substantially altered the microbial community composition, with halotolerant taxa such as Raineyella and Sphingopyxis becoming more abundant and salinity-driven shifts in nitrification processes indirectly affecting phosphorus removal under high-salinity conditions. These findings demonstrate SF-AGS exhibits robust salinity tolerance and its promising applicability for high-salinity wastewater treatment.

In the design of charge pump phase-locked loop (CP-PLL) systems, inherent CMOS device characteristics give rise to nonideal effects namely current mismatch, charge sharing, and clock feed through within the charge-pump architecture, which critically degrade loop performance. This paper proposes an improved low mismatch charge-pump topology. A triple bias dual feedback mechanism is combined with a bootstrap differential-switch technique to enhance current matching accuracy. In the first stage, a source-gate-connected current mirror generates the reference current. In the second stage, a high gain operational amplifier implements a dynamic negative feedback network to stabilize the PMOS and NMOS branch currents. And in the third stage, complementary switches and an op amp loop continuously adjust the operating point of the main current-source branch. These three parallel subsystems form a closed loop regulation mechanism. Experimental results indicate that, under a 1.8 V supply, the proposed structure achieves a charge or discharge current mismatch ratio of less than 0.1%, representing an order of magnitude improvement over conventional designs.

This paper proposes a low-temperature-drift bandgap reference voltage source featuring wide-temperaturerange high-order compensation with process-corner-adaptive trimming. A segmented temperature compensation scheme is achieved through controlled switching between multiple distinct current paths. To mitigate the impacts of process corner variations on circuit performance caused by parameter variations in bipolar junction transistors and resistors, a trimming circuit is specifically developed. An innovative logic selection structure is designed to enable co-optimization between the segmented compensation architecture and trimming circuit. A high-ss bipolar junction transistor input-stage operational amplifier structure is utilized to mitigate flicker noise. Implemented in 0.18-mu m BCD technology, the circuit achieves a 1.2V output voltage exhibiting a temperature coefficient of 0.47 ppm/C-circle over -40(circle)C to 175(circle)C The worst-case temperature drift coefficient across process corners remains below 1.95 ppm/C-circle, while maintaining an integrated noise of 20 mu V-rms within 0.1-10Hz frequency range.

Efficient battery management system (BMS) monitoring and accurate battery state estimation are inseparable from precise battery models and model parameters. Because of the multi-time scale dynamic characteristics of the battery system, there are still challenges in the modeling and parameter identification accuracy of the battery equivalent circuit model (ECM) in this case. This article proposes a multi-time scale parameter identification algorithm based on multiresolution analysis (MRA) of discrete wavelet transform (DWT), which is used for closed-loop estimation of battery ECM parameters corresponding to different electrochemical dynamic effects. The ECM of the battery at multiple time-scales is determined by the distribution of relaxation times (DRTs) method, and MRA decomposition is performed on the battery signal to determine the separated and decoupled model parameters. The open-circuit voltage (OCV) is used as a slow time-scale model parameter and does not require offline state-of-charge (SOC)-OCV calibration. Under the urban dynamometer driving scheme (UDDS) experiment, the estimation results of ECM parameters, terminal voltage, and SOC using the proposed algorithm were compared with those obtained using different implementation methods. The root mean square error (RMSE) results show that the algorithm can accurately estimate the terminal voltage, OCV, and SOC of the battery, with estimation errors of 0.966, 2.58mV, and 0.1263%, respectively.

This paper presents a Bandwidth-Enhanced Nested Oversampling Successive Approximation (BE-Nested-OSA) readout architecture for low-power, high-precision MEMS capacitive accelerometers. The proposed structure integrates the high-order error suppression of Nested-OSA with the bandwidth optimization of BE-OSA. By introducing auxiliary amplifiers to construct nested feedback paths, gain error is effectively mitigated without requiring high-gain amplifiers. By introducing a continuous-time feedback capacitor and a discrete-time compensation loop, system bandwidth is extended and noise floor is reduced without increasing amplifier transconductance. The Simulation results show that BE-Nested-OSA achieves near-ideal accuracy at a 1 MHz sampling rate, with superior low-frequency 1/f noise suppression and mid-band noise comparable to BE-OSA. Despite a slight power increase, the architecture achieves favorable trade-offs among precision, noise, and efficiency.

Compressed sensing theory offers a novel paradigm for high-dimensional signal processing through sparse sampling and nonlinear reconstruction. However, its efficient hardware implementation remains constrained by the memory-computation separation inherent in the von Neumann architecture. In this paper, a TaO (Tantalum Oxide) memristor-based in-memory compressed sensing framework was proposed. By establishing a MATLAB-LTspice co-simulation platform and performing circuit-algorithm co-optimization, a cross-layer equivalence is constructed between the sparse weight matrix in compressed sensing and the conductance parameters of the memristor array. This approach enables analog-domain computation with accuracy comparable to digital circuits while eliminating data transfer latency. Experimental results demonstrated that the average reconstruction signal-to-noise ratio (ARSNR) of the memristorbased system deviates by no more than 1 dB from the ideal reconstruction.

This work developed a novel ozone (O-3)/Mn-Ce/gamma-Al2O3(MC)/peroxymonosulfate (PMS) process for the degradation of ofloxacin (OFX) in wastewater. The combination of O-3, MC, and PMS exhibited a significant synergistic effect, achieving 95.17 % degradation and 76.05 % TOC removal in 50 min, which were markedly higher than those of the O-3/PMS, MC/PMS, and O-3/MC processes. OFX was effectively mineralized (> 66.35 %) across a wide pH range (3.0-11.0), with minimal inhibition from common anions (CO32-, NO3-, Cl-). Additionally, the O-3/MC/PMS process maintained stable performance in continuous real pharmaceutical wastewater treatment with over 60 % TOC removal and low metal leaching (< 20 mu g L-1), confirming strong practical applicability. Electron paramagnetic resonance (EPR), quenching, and excess scavenger-based quantification experiments experiments indicated that HO center dot, SO4 center dot(-), O-2 center dot(-), and O-1(2) contributed to the degradation process. Specifically, the generation rates of HO center dot (12.26 mu M min(-)(1)) and SO4 center dot(-) (16.53 mu M min(-)(1)) were dramatically enhanced in the O-3/MC/PMS process, about 3-fold and 9-fold higher than those of the O-3/PMS process, respectively. The role of synergistic effect in accelerated electron transfer and enhanced reactive oxygen species (ROS) generation was deeply investigated by in situ characterization and density functional theory (DFT) calculation. The Mn-Ce centers facilitated the interfacial coadsorption of O-3 and PMS. PMS bonded to surface Ce sites to form metal peroxides (equivalent to Ce-OOSO3-) as electron donors, while O-3 accepted electrons from both the equivalent to Ce-OOSO3- and Mn sites. These findings elucidated the synergistic mechanisms of O-3/PMS on the MC catalyst, offering valuable insights for advancing the development of heterogeneous O-3/PMS processes.

This paper presents a novel wide-temperature and low-current mismatch charge pump circuit for modern communication systems requiring high-performance phase locked loop (PLL). When applied in a CMOS PLL, the proposed charge pump effectively suppresses phase error induced by non-idealities in the charge pump, thereby enhancing the overall PLL performance. The design employs a bias circuit to generate a temperature-independent charge pump current, effectively reducing phase error fluc-tuations caused by temperature variations. The core circuit of the charge pump incorporates transmission gates and an operational amplifier to achieve excellent current matching characteristics. Fabricated using a 180 nm CMOS process. Simulation results show that over a temperature range from -40°C to 175 °C, the output current variation remains within 49.86 μA to 50.16 μA, with a temperature coefficient of31 ppm/°C. Compared with conventional push-pull input charge pumps, the proposed design significantly extends the output voltage range under 0.01% current mismatch from 0.32 V to 2.4 V, achieving a 24.24% improvement.

As the fundamental timing reference in integrated circuit systems, the clock signal is often required to have extremely short start-up time. Existing methods to improve start-up time typically employ external signal control to drive the crystal, which evidently compromises the circuit's integration and power efficiency. This paper presents a quick start-up 32.768 kHz crystal oscillator with adaptive dual-mode switching. During the start-up phase, the negative resistance boosting (NRB) technique is applied by a multistage amplifier to shorten the start-up time. Transition from boost-mode to steady-mode is controlled through amplitude detection, facilitating dual-mode adaptive operation without the reliance on external clocks. This approach enhances the robustness and integration of the circuit while effectively mitigating the increase in power consumption typically associated with NRB. As a validation, the proposed technique is incorporated into the design of a crystal oscillator and implemented in 180 nm CMOS technology, where experiments show that the start-up time and the power consumption of the core starting circuit are 116 ms and 1.03 mu W, respectively.

Analog-to-digital converters (ADCs) are widely used as a bridge between analog and digital domains in communication systems. With the rapid development of smart sensors, the demand for high-precision ADCs is increasing day by day. In recent years, ∑-A modulators have been widely adopted in high-performance ADCs owing to their high resolution and strong noise immunity. However, extreme high-temperature working environments such as aerospace and oil exploration place higher requirements on the working performance of modulators at high temperatures. To address the issue that traditional high-order $\Sigma-\Delta$ modulator systems tend to lose stability at high temperatures, this paper constructs an optimized high-order $\Sigma-\Delta$ modulator model. This model adds local negative feedback, gain control and other modules to the traditional fourth-order single-bit quantized modulator model to improve the performance and stability of the system at high temperatures. Aiming at the problem of time domain and frequency domain performance degradation of sampling switches in high temperature environments, this paper proposes an optimized circuit design scheme. This scheme significantly enhances the system's operational stability under high-temperature conditions through an improved sampling switch architecture. In the design of the sampling switch, a bootstrap structure is used to enhance the gate drive capability. At the same time, redundant transistors and virtual transistor technology are introduced to effectively improve the switching linearity and suppress the channel charge injection effect. The optimized modulator system is simulated. At 175°C, the SNDR of the modulator is 105.2dB and the ENOB of the modulator is 17.79bits. The simulation results show that the added optimized structure can effectively improve the performance of the modulator at high temperature.

An Au–Pt nanoalloy photocatalyst operates effectively and selectively for the oxidation of polyols under visible light.

Metal cyanamides represent a category of inorganic functional materials with superior activity and stability. However, their potential application in plastic recycling remains unexplored. This study mainly focused on the utilization of zinc cyanamide (ZnNCN) as a poly(ethylene terephthalate) (PET) glycolysis catalyst. The PET glycolysis process is demonstrated to be promoted by a synergistic catalysis effect. Under optimal conditions, 100% PET conversion and 92.3% bis(2-hydroxyethyl) terephthalate (BHET) yield were achieved. Furthermore, successful depolymerization toward various real commercial PET wastes and excellent recycling performance proved promising application prospects. DFT study and NMR characterization have revealed that the remarkable catalytic activity stems from the synergistic effect of the Lewis acid site and hydrogen bonds between ethylene glycol (EG) and the catalyst. This work provides valuable insight into the application of metal cyanamides, paving the way for the development of effective catalysts for PET recycling.

The tensor data format has become popular due to the large number of CNN parameters and the increased storage requirements. While it has the potential to address various scientific and engineering challenges, applying it to limitedre-source devices can be difficult. Therefore, the primary focus of this research is to utilize hardware to enhance the speed of neural network prediction while maintaining the accuracy of the original network. We have implemented relevant modules, such as the tensor multiplied matrix (TMM) module and the matrix singular value decomposition (SVD) method, using ZYNQ FPGA. We constructed the LeNet-5 model, extracted its convolution layer parameters, decomposed them on the hardware side, and compared the outcomes. By verifying the design on an FPGA, the final results show that a 10.5 times acceleration ratio is achieved compared to the CPU platform.

The accuracy of estimating lithium battery internal parameters and state of charge (SOC) is closely related to the appropriate model and efficient algorithm. The real-time online estimation of the open-circuit voltage (OCV) model and the model parameters for multiple time scales are particularly significant. This article proposes an online dual time-scale recursive least squares (DTRLS) for closed-loop estimation of resistor-capacitor (RC) network parameters, OCV, and SOC of equivalent circuit model (ECM). The dynamic characteristics of the battery system are analyzed at multiple time scales. The model parameters are separated and decoupled to overcome the parameter deviation and numerical issue in the time domain of the classical recursive least squares (RLS) method. In the slow time scale, the OCV parameter is coestimated without additional offline open-loop tests to improve the accuracy of SOC. Under the Urban Dynamometer Driving Schedule (UDDS) test, the ECM parameters and terminal voltages estimated by the proposed DTRLS method are compared with that of classical forgetting factor RLS (FFRLS). The root-mean-squared error (RMSE) results show that the online closed-loop DTRLS method improves the estimation accuracy of terminal voltage, OCV, and SOC by 0.97%, 3.2%, and 2.06%, respectively.

Algal-bacterial granular sludge (ABGS) composed of microalgae and aerobic granular sludge, is a sustainable and promising technology for wastewater treatment. However, the formation mechanism of ABGS has not been clearly defined, and the direct formation of ABGS in saline wastewater has rarely been investigated. This study proposed novel insights into the granulation process of ABGS by assembling the algal barrier, which was successfully cultivated directly in saline wastewater. The results concluded that ABGS with the algal barrier maintained a higher biomass (MLSS of 7046 ± 61 mg/L), larger particle sizes (1.21 ± 0.06 mm), and better settleability (SVI30 of 46 ± 1 mL/g), enabling efficient pollutants removal. Soluble microbial products (SMP) were found to be closely related to the emergence of the algal barrier. In addition, under salinity stress, the high production of extracellular polymeric substances (EPS, 133.70 ± 1.40 mg/g VSS), specifically TB-EPS (90.29 ± 1.12 mg/g VSS), maintained a crucial role in the formation of ABGS. Further analysis indicated that biofilm producing bacteria Pseudofulvimonas and filamentous eukaryote Streptophyta were the key players in ABGS formation with the algal barrier. Furthermore, the enhancement of key genes and enzymes involved in nitrogen metabolism, TCA cycle, and polysaccharide metabolism suggested a more robust protective effect provided by the algal barrier. This study is expected to advance the application of simultaneous ABGS formation and pollutant removal in wastewater.

Upcycling of waste poly(ethylene terephthalate) (PET) into valuable products represents a promising avenue for advancing carbon neutrality and circular economy. Here, we demonstrate a modular strategy for converting waste PET into glycolic acid (GA) and 2,4-pyridine dicarboxylic acid (2,4-PDCA), achieving an upcycling process and 45% reduction in greenhouse gas emissions. We conducted comprehensive studies on PET hydrolysis, PET-derived ethylene glycol (EG) photooxidation, and PET-derived terephthalic acid (TPA) bioconversion. Utilizing a plasmon-active CuPt nanoalloy, EG oxidation proceeds at mild conditions with impressive EG conversion (94.78%) and GA yield (71.98%). Two Escherichia coli strains were employed to convert TPA into 2,4-PDCA, achieved a 91.03% molar yield. This work successfully accomplishes the comprehensive utilization of waste PET through an environmentally friendly and economically viable strategy, leading to a significant reduction in PET plastic pollution while simultaneously generating substantial economic benefits.

Efficient coenzyme regeneration in biohybrids can help overcome the challenge of insufficient reducing power in biohydrogen production, but the performance of biohybrids is often hampered by light-dependent and inefficient photoelectron transmembrane transfer. Here, we present an intracellular hybrid system composed of gold-palladium nanoalloys and Clostridium butyricum, which demonstrates efficient dark-catalyzed coenzyme regeneration, thereby enhancing hydrogen production capabilities. By utilizing triethanolamine (TEOA) as the electron donor, the hybrid system achieved a maximum hydrogen production of 2.14 mol of H-2mol(-1) glucose, resulting in a remarkable increase of 47.37%. The Au-Pd nanoalloy regenerated intracellular NADH through chemical catalysis with TEOA as the electron donor, which was confirmed by increased reducing power levels and pronounced peak currents. Consequently, the hybrid system had a higher reducing power level, which enhanced the hydrogen-producing activity of the pyruvate formate-lyase (PFL) and NADH-ferredoxin oxidoreductase (NFOR) pathways. The PFL pathway oxidizes pyruvate, while the NFOR pathway directly oxidizes NADH. Pyruvate, a substrate required for hydrogen production in the PFL pathway, is generated through a combination of glucose phosphate transfer and phosphoenolpyruvate (PEP) dephosphorylation. This study offers theoretical guidance for the development of a dark hybrid system of nanocatalysts and microbes that can effectively produce biohydrogen and be used for other applications.

Aerobic granular sludge (AGS) is a powerful biotechnological tool capable of treating multiple pollutants simultaneously. However, the granulation process and pollutant removal efficiency still need to be further improved. In this study, Fe2O3- and MnO2-surface-modified straw foam-based AGS (Fe2O3@SF-AGS and MnO2@SF-AGS), with an average particle size of 3 mm, were developed and evaluated. The results showed that surface modification reduced the hydrophobic groups of carriers, facilitating the attachment and proliferation of microorganisms. Notably, MnO2@SF-AGS showed excellent granulation performance, reaching a stable state about one week earlier than the unmodified SF-AGS. The polymeric substance content of MnO2@SF-AGS was found to be 1.28 times higher than that of the control group. Furthermore, the removal rates for NH4+-N, TN, and TP were enhanced by 27.28%, 12.8%, and 32.14%, respectively. The bacterial communities exhibited significant variations in response to different surface modifications of AGS, with genera such as Saprospiraceae, Terrimonas, and Ferruginibacter playing a crucial role in the formation of AGS and the removal of pollutants specifically in MnO2@SF-AGS. The charge transfer of metal ions of MnO2@SF promotes the granulation process and pollutant removal. These results highlight that MnO2@SF-AGS is an effective strategy for improving nitrogen and phosphorus removal efficiency from wastewater.

Norfloxacin (NOR) wastewater with a significant corrosive effect on human tissues requires deep treatment before discharge. Peroxydisulfate (PDS) activation with catalysts is an effective advanced method for wastewater treatment, and the key to its success lies in the design, synthesis, and preparation of efficient, stable, and easily prepared catalysts. In this study, magnetic catalysts of MnFe2O4-AC were synthesized through the hydrothermal method as the basic material activated PDS. The crystal structures were characterized using various techniques, including SEM-EDS, XRD, FT-IR, and XPS. The high magnetic properties of the prepared catalysts made them easily recoverable after wastewater treatment. In addition, the study investigated the effects of operating conditions on the treatment of NOR wastewater within the Electrochemistry/MnFe2O4-AC/PDS (EC/MnFe2O4-AC/ PDS) system. Under the optimal conditions of current intensity of 0.15 A, catalyst dosage of 0.100 g/L, PDS concentration of 10 mmol/L, and NOR concentration of 5 mg/L, the removal rate of NOR was 97.33 %. The increased specific surface area of MnFe2O4-AC catalysts, when compared to MnFe2O4 catalysts, benefited the activation of PDS. MnFe2O4-AC catalysts showed a good stability in activating PDS for NOR wastewater treatment without causing secondary pollution in repeated use experiments. Finally, the NOR degradation was attributed to both the generation of free radicals (SO4 center dot- and center dot OH radicals) and non-radical processes (electrode oxidation).

The selective cleavage of lignin C-C bonds is a highly sought-after process with the goal of obtaining low-molecular-weight aromatic chemicals from renewable resources. However, it remains a challenging task to achieve under mild conditions. Photocatalysis is a potentially promising approach to address this issue, but the development of efficient photocatalysts is still in progress. In this study, we introduce the heterostructured TiO2@g-C3N4photocatalyst for the development of a visible light photocatalytic procedure for the selective cleavage of lignin C-C bonds under mild conditions. The photocatalyst displays favourable visible light absorption, efficient charge separation efficiency, and promising reusability. A typicalβ-O-4 dimer model, 2-phenoxy-1-phenylethanol, was effectively (96.0% conversion) and selectively (95.0 selectivity) cleaved under visible light at ambient conditions. This photocatalytic procedure was also effective when subjected to solar irradiation or other lignin dimer models withβ-O-4 orβ-1 linkages. This reaction occurred through a Cβ-centred radical intermediate and a six-membered transition state with photogenerated holes as the primary active species. The Cα-OH oxidative dehydrogenation of the substrate could also take place but was a relatively minor route. This study provides a new photocatalytic procedure for visible-light-driven lignin valorisation and sheds light on the design of high-performance nanocomposite photocatalysts for C-C bond cleavage.