Simultaneous Output Research Articles

Water-Triboelectrification-Complemented Moisture Electric Generator.

Energy harvesting from ubiquitous natural water vapor based on moisture electric generator (MEG) technology holds great potential to power portable electronics, the Internet of Things, and wireless transmission. However, most devices still encounter challenges of low output, and a single MEG complemented with another form of energy harvesting for achieving high power has seldom been demonstrated. Herein, we report a flexible and efficient hybrid generator capable of harvesting moisture and tribo energies simultaneously, both from the source of water droplets. The moisture electric and triboelectric layers are based on a water-absorbent citric acid (CA)-mediated polyglutamic acid (PGA) hydrogel and porous electret expanded polytetrafluoroethylene (E-PTFE), respectively. Such a waterproof E-PTFE film not only enables efficient triboelectrification with water droplets' contact but also facilitates water vapor to be transferred into the hydrogel layer for moisture electricity generation. A single hybrid generator under water droplets' impact delivers a DC voltage of 0.55 V and a peak current density of 120 μA cm-2 from the MEG, together with a simultaneous AC output voltage of 300 V and a current of 400 μA from the complementary water-based triboelectric generator (TEG) side. Such a hybrid generator can work even under harsh wild environments with 5 °C cold and saltwater impacts. Significantly, an optical alarm and wireless communication system for wild, complex, and emergency scenarios is demonstrated with power from the hybrid generators. This work expands the applications of water-based electricity generation technologies and provides insight into harvesting multiple potential energies in the natural environment with high output.

Dual-Arm Space Robot On-Orbit Operation of Auxiliary Docking Prescribed Performance Impedance Control

The impedance control of a dual-arm space robot in orbit auxiliary docking operation is studied. First, for the closed-chain hybrid system formed by the dual-arm space robot after capture operation, the dynamic equation of position uncontrolled and attitude controlled is established. The second-order linear impedance model and second-order approximate environment model are established for the problem of simultaneous output force/pose control of the end of the manipulator. Then, aiming at the transient performance control requirements of the dual-arm space robot auxiliary docking operation in orbit, a sliding mode controller with equivalent replacement of tracking errors is designed by introducing Prescribed Performance Control (PPC) theory. Next, Radial Basis Function Neural Networks (RBFNN) are used to accurately compensate for the modeling uncertainties of the system. Finally, the stability of the system is verified by Lyapunov stability determination. The simulation results show that the attitude control accuracy is better than 0.5°, the position control accuracy is better than 10−3 m, and the output force control accuracy is better than 0.5 N when it reaches 30 N. It also indicated that the proposed control algorithm can limit the transient performance of the controlled system within the preset range and achieve high-precision force/pose control, which ensures a more stable on-orbit auxiliary docking operation of the dual-arm space robot.

Mixed‐Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management

ABSTRACTMany optimal control problems require the simultaneous output of discrete and continuous control variables. These problems are typically formulated as mixed‐integer optimal control (MIOC) problems, which are challenging to solve due to the complexity of the solution space. Numerical methods such as branch‐and‐bound are computationally expensive and undesirable for real‐time control. This article proposes a novel hybrid‐action reinforcement learning (HARL) algorithm, twin delayed deep deterministic actor‐Q (TD3AQ), for MIOC problems. TD3AQ leverages actor‐critic and Q‐learning methods to manage discrete and continuous action spaces simultaneously. The proposed algorithm is evaluated on a plug‐in hybrid electric vehicle (PHEV) energy management problem, where real‐time control of the discrete variables, clutch engagement/disengagement and gear shift, and continuous variable, engine torque, is essential to maximize fuel economy while satisfying driving constraints. Simulation results show that TD3AQ achieves near‐optimal control, with only a 4.69% difference from dynamic programming (DP), and outperforms baseline reinforcement learning algorithms for hybrid action spaces. The sub‐millisecond execution time indicates potential applicability in other time‐critical scenarios, such as autonomous driving or robotic control.

Closed-loop workload input–output control of production systems: A hybrid simulation study

Workload Control (WLC) is a method of adjusting the overall workload in the production system by controlling the input of production orders and the output of produced items. It contributes to the predictability of lead times and more accurate delivery date commitments in make-to-order manufacturing. Input control relates to order release and output control to capacity adjustment. In a recent previous work, a novel approach to integrated input–output control has been proposed: a dynamic closed-loop model where automatic feedback control is added and where order release and capacity decisions are based on the observed shop floor state. In this paper, this approach is further investigated in a more realistic hybrid simulation setting, combining discrete-time controllers and a discrete-event model of a manufacturing shop. The hybrid model was applied to two different systems: an elementary 4-machine shop floor (as a proof of concept) and a more complex real system, a manufacturing cell of cylinder liners. The results show that simultaneous input–output control is effective and enables the systems to maintain WIP balance, absorb demand fluctuations, and effectively reject disturbances. The simulations of the complex system showed that it might be advantageous to provide more local or more global information to the controllers, depending on the variability of the processing times and on the managerial focus. Closed-loop discrete-event models for WLC production control are unprecedented. Also, a simultaneous input and output control setting is much less reported in WLC literature than settings with input control only (i.e., order release only). This paper contributes in both ways.

Difference‐Frequency Generation of 0.2‐mJ 3‐Cycle 9‐µm Pulses from Two 1‐kHz Multicycle OPCPAs

AbstractIntense long‐wave infrared (LWIR) femtosecond pulses within the 8−14 µm atmospheric window present an array of applications, such as in strong‐field physics, ultrafast nonlinear spectroscopy, and self‐guided atmospheric propagation. However, the realization of an LWIR source capable of delivering millijoule‐class energy, few‐cycle duration, and kHz repetition rate concurrently remains challenging. Here, such an LWIR source via the combination of different nonlinear parametric processes is reported, driven by a 1 kHz Yb:YAG thin‐disk laser. The system comprises two parallel multi‐cycle optical parametric chirped‐pulse amplifiers (OPCPAs) operating at 2.3 and 3.1 µm, respectively, alongside a stage of ZnGeP2‐crystal‐based difference‐frequency generation (DFG). The resulting 9 µm DFG pulses have a record energy of 0.21 mJ, a 3‐cycle duration, a 1 kHz repetition rate, and long‐term energy stability. The simultaneous output of three synchronized intense lasers at short‐wave infrared (2.3 µm), mid‐wave infrared (3.1 µm), and LWIR (9 µm) renders the source particularly appealing for multicolor ultrafast applications.

Ultrasensitive and multiplexed Gastric cancer biomarkers detection with an integrated electrochemical immunosensing platform

Developing immunosensing platforms capable of simultaneously detecting multiple cancer markers is crucial for clinical diagnosis and biomedical research. Here, we introduce a novel dual-mode electrochemical biosensing assay platform capable of detecting two gastric cancer biomarkers: pepsinogen I (PG I) and pepsinogen II (PG II). Methylene blue (MB) and Prussian blue (PB) were used as dual signal sources to label PG I and PG II, respectively. The platform integrates an ARM STM32F411 microcontroller and an AD5941 analog front-end, which not only facilitates cyclic voltammetry (CV) and differential pulse voltammetry (DPV) with efficacy comparable to commercial electrochemical workstations but also offers data collection and synchronous analysis capabilities, allowing simultaneous output of PG I and PGR (PG I/PG II) values. Equipped with an interactive screen for operational control and result display, the immunosensing platform provides linear detection ranges for PG I (5 pg/mL–100 ng/mL) and PG II (50 pg/mL–200 ng/mL), enabling rapid detection within 5 min. It demonstrates excellent sensitivity and selectivity when comparing serum samples from healthy individuals and gastric cancer patients. The dual-marker detection platform significantly enhances early diagnosis and screening of gastric cancer, offering substantial improvements over single-marker assays. Furthermore, this platform shows potential for detecting multiple biomarkers in various diseases, highlighting its utility for biomedical applications.

A simultaneous energy self-sufficient desalination and energy output process based on a novel membrane stack design

Electrodialysis (ED) uses electric energy to conduct the desalination, while bipolar membrane reverse electrodialysis (BMRED) converts the chemical energy containing in acids and alkalis into electric energy. Here, a novel membrane stack (BMRED-ED) based on BMRED and ED was designed to realize the simultaneous energy self-sufficient desalination and energy output process. The stack was studied in two different operation modes systematically. Results of the open-loop operation mode demonstrate that the maximum power density (Pd,max) can obtain a highest value of 4.53 W·m−2, and the maximal average apparent permeability (α) between the open circuit voltage (OCV) and theoretical electromotive force (EMF) is 92.23 %. Results of the closed-loop operation mode indicate that the final desalination ratio (ξdel) can attain as high as 89.57 %, and the corresponding energy utilization ratios of the desalination (ηdes) and the energy output (ηout) are 0.90 % and 14.00 %, respectively. Moreover, the effect of desalinated salt solution concentrations and discharging current densities on the closed-loop operation performance are much more remarkable than that of cation species in the alkali and salt solutions. This work provides a feasible and competitive strategy for simultaneous energy self-sufficient desalination and energy output.

A Clinically Feasible Diagnostic Electrochemical Micronano Motors Biosensor Built on Miniature Swimmer for Multiplex Detection and Grading of Breast Cancer Biomarkers.

Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and Ki67 are four crucial biomarkers used in the clinical diagnosis of breast cancer. Accurate detection of these biomarkers is essential for an effective diagnosis and treatment. MOF-based micronano motors (MOFtors) are promising for various applications, including environmental remediation, targeted nanosurgery, and biomarker detection. This paper presents a clinically feasible diagnostic electrochemical micronano motor biosensor, built on a miniature swimmer, for the multiplex detection and grading of breast cancer biomarkers. We designed a biosensor, named MOFtor-MSEM, incorporating aptamers and antibodies functionalized on SiO2@Co-Fe-MOF, which acts as a miniature swimmer in solution. The SiO2@Co-Fe-MOF serves as the body, while complementary double-chain-linked antibodies function as paddles. In a homogeneous solution, when a positive voltage is applied to the working electrode, the electrostatic interaction between the neutral SiO2@Co-Fe-MOF and the negatively charged complementary double-linked antibody causes the antibody to move toward the electrode and then regress due to water resistance. This back-and-forth motion propels the miniature swimmer, enabling it to move the target analyte through the solution. The sensor features an automatic "sample-amplifying signal-output" process, achieving simultaneous signal amplification and output of four electrochemical signals on a single nanomaterial, a significant challenge in electrochemical sensing. The biosensor boasts a short detection time of 40 min, compared to approximately 1 week for current clinical tissue testing. Additionally, the bioplatform selectively detects HER2, ER, Ki67, and PR in the range of 0-1500 pg/mL, with detection limits of 0.01420, 0.03201, 0.01430, and 0.01229 pg/mL, respectively.

Integrated Microfluidic-Transistor Sensing System for Multiplexed Detection of Traumatic Brain Injury Biomarkers.

Traumatic brain injury (TBI) is widely recognized as a global public health crisis, affecting millions of people each year, leading to permanent neurologic, emotional, and occupational disability, and highlighting the urgent need for rapid, sensitive, and early assessment. Here, we design a novel and simple lithography-free method for preparing dual-channel graphene-based field-effect transistors (G-FETs) and integrating them with microfluidic channels for simultaneously multiplexed detection of key blood TBI biomarkers: neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP). The G-FET utilizes an ingenious dual-channel electrode array design, where the source is shared between channels and the drains are independent of each other, which is the key to achieving simultaneous output of dual detection signals. At the same time, the microfluidic chip realizes microscale fluidic control and fast sample response time. This integrated detection system shows excellent sensitivity in biological fluids for the TBI biomarkers with detection limits as low as 55.63 fg/mL for NFL and 144.45 fg/mL for GFAP in phosphate-buffered saline (PBS) buffer, respectively. Finally, the clinical sample analysis shows promising performance for TBI detection, with an area under the curve (AUC) of 0.98 for the two biomarkers. And the combined dual-protein assay is also a good predictor of intracranial injury findings on computed tomography (CT) scans (AUC = 0.907). The integrated microfluidic G-FET device with a dual-signal output strategy has important potential for application in clinical practice, providing more comprehensive information for brain injury assessment.

A single-IC realizable, electronically tunable, OTA-based full-wave rectifier with simultaneous positive and negative outputs

A new voltage-mode full-wave rectifier (FWR) is proposed in this study. The proposed FWR employs two single-output operational transconductance amplifiers (OTAs), four diodes, and two grounded resistors. It has high input impedance and does not need any extra bias current or voltage sources. It simultaneously provides positive and negative rectified output signals over separate output terminals. The amplitudes of both output signals can be adjusted by varying the resistor values or the transconductance (gm) values of the OTAs. It does not include any floating passive elements, so it is suitable for integrated circuit (IC) implementations. The proposed FWR can be implemented by using a single commercially available IC such as LM13700. However, the proposed FWR has a single matching condition for gm values of the OTAs. Several simulation and experimental test results are added to confirm the theory and show the operability of the proposed circuit.

Asynchronous verifiable information dispersal protocol of achieving optimal communication

The evolution of distributed systems necessitates robust protocols to facilitate secure and efficient data storage and retrieval amid challenges like asynchronous communication and adversarial threats. However, achieving the theoretical lower bounds of communication cost in Asynchronous Verifiable Information Dispersal (AVID) protocols remains an unresolved issue, prompting the need for innovation in this domain.In this paper, we propose a new AVID protocol designed to optimize communication cost. In contrast to previous approaches, we leverage vector commitment (VC) technology combined with erasure codes, replacing conventional hash tree structures or error correction codes. This approach results in significant O(logn) communication savings. Additionally, our research reveals that while ensuring simultaneous output from all honest nodes is desirable to satisfy Totality, it is not necessary for all nodes to receive corresponding shares or proofs during the Dispersal phase. This insight allows us to circumvent the common O(n2κ) communication overhead.Our AVID protocol achieves an optimal dispersal cost of O(|M|+n2) compared to the prior best of O(|M|+κn2). Furthermore, each node in our protocol incurs a storage cost of O(|M|/n+κ) bits, while the retrieval cost per client is O(|M|+nκ).

Fully Symmetrical Hybrid PV Converter With Low Common-Mode Noise

Hybrid converters with multiports (simultaneous DC and AC outputs) are promising solutions to reduce conversion stages from solar energy to various loads, and thereby increasing the utilization of residential PV systems. Considering common-mode noises suppression and high reliability, this paper proposes a fully symmetrical hybrid PV converter concept with two DC ports and one AC port. The proposed converter adopts symmetrical boost impedances at the low-voltage DC port and symmetrical synchronization rectifier switches at the high-voltage DC port. In addition, a symmetrical AC-decoupling inverter, i.e., highly efficient and reliable inverter concept (HERIC), operates as either a boost switch or an inverter to perform DC-DC and DC-AC conversions simultaneously. Combined with a dedicated modulation method with deadtime, the proposed converter has low leakage currents for grid-connected PV applications, low CM noise as a DC-DC converter, and flexible power control capability. Experiments have been carried out on a prototype to verify the effectiveness of the proposed converter.

Plug‐In Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous‐Discrete Reinforcement Learning

Energy management strategy (EMS) is a key technology for plug‐in hybrid electric vehicles (PHEVs). The energy management of certain series–parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement. Herein, a control‐oriented model is established for a series–parallel plug‐in hybrid system with clutch engagement control from the perspective of mixed‐integer programming. Subsequently, an EMS based on continuous‐discrete reinforcement learning (CDRL), which enables simultaneous output of continuous and discrete variables, is designed. During training, state‐of‐charge (SOC) randomization is introduced to ensure that the hybrid system exhibits optimal energy‐saving performance in both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy is verified by comparing EMS based on charge‐depleting charge‐sustaining (CD‐CS) with rule‐based clutch engagement control and dynamic programming (DP). In the simulation results, it is shown that, under a high SOC, the CDRL strategy proposed in this article can improve energy efficiency by 8.3% compared to CD‐CS, and the energy consumption is just 6.6% higher than the global optimum based on DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively.

MO-CCCII-Based Single-Input Multi-Output (SIMO) Current-Mode Fractional-Order Universal and Shelving Filter

This study introduces an innovative filter topology capable of providing simultaneous positive and negative gain outputs for one-fractional order LP, with high-pass, all-pass, and fractional-order shelving filter responses. The circuit, utilizing multi-output second-generation current-controlled conveyors, stands out as the first to deliver ten outputs, incorporating both integer and fractional-order filter responses, without requiring additional components. Its current-mode design simplifies the process, employing minimal active and grounded passive elements, making it appropriate for low-voltage/low-power applications. The filter utilizes fifth-order Oustaloup approximation and Foster type-I RC networks for fractional-order capacitors, providing enhanced control over the transition slope. PSpice simulations confirmed a 1 kHz cut-off, showcasing low power consumption, minimal noise, and a wide dynamic range, positioning the filter as suitable for sensors, control, and acoustic applications.

Fast prediction of diverse rare ginsenoside contents in Panax ginseng through hyperspectral imaging assisted with the temporal convolutional network-attention mechanism (TCNA) deep learning

Combining hyperspectral imaging (HSI) with deep learning algorithms provides an effective and fast approach for evaluating the quality of food and agricultural by-products. This study comprehensively determined the quality of ginseng (Panax ginseng C. A. Meyer), an important medicinal and nutritional food, by evaluating the contents of diverse rare ginsenosides (RGs) using HSI technology. The results indicated that the combination of HSI with the deep learning temporal convolutional network-attention mechanism (TCNA) model achieved the best results in predicting the contents of six types of RGs (Rh1, Rh2, F1, Rg3, F4, and Rk1) simultaneously and effectively. Especially, the content detection of the six RGs based on the effective wavelengths showed that the TCNA model achieved coefficient of determination (R2) values above 0.890 and relative percentage deviation (RPD) values higher than 3.0, demonstrating excellent model performance. Meanwhile, the use of effective wavelengths makes the results of the TCNA model have better interpretability, and the simultaneous output of six RGs contents significantly improves prediction efficiency. The HSI assisted with the TCNA algorithm provides a rapid and effective detection approach for simultaneously predicting the content of diverse quality indicators. All these results will provide a new reference for developing convenient and rapid HSI equipment in the food and agricultural industry for direct and comprehensive quality inspection in markets in the future.

Self-powered Pb(II) removal driven by Zn-air batteries using N, P-doped active straw carbon as cathode

Electrochemical systems have been widely applied in heavy-metal ions (HMIs) pollution control, while their applications are greatly restricted by excessive energy consumption. Self-powered environmental remediation systems (SERSs) can shed the energy reliance to achieve HMIs decontamination and simultaneous electricity output through electrokinetic migration. In this work, SERSs based on Zn-air batteries (ZABs) were achieved to remove bivalent lead (Pb(II)) by circulating effluent as electrolyte. Straw biomass derived N, P functional active nanocarbon (NP-ASC) with hierarchical porosity and electron modulation effect was designed for achieving high oxygen reduction reaction (ORR) activity and efficient Pb(II) uptake. SERSs with electrode active area of 1 cm2 realize outstanding Pb(II) removal efficiency (98.1 %) and electricity generation (67.5 mWh∙cm−2) in simulated sewage. ZABs assembled with NP-ASC delivered specific capacity of 795 mAh∙g−1 and cycling stability for 340 h. This study provides a guide for designing a novel SERS using functionalized biomass nanocarbon.

Neuromorphic Imaging With Joint Image Deblurring and Event Denoising.

Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic scene with high temporal precision and responds with asynchronous streaming events as a result. It also often supports a simultaneous output of an intensity image. Nevertheless, the raw events typically involve a large amount of noise due to the high sensitivity of the sensor, while capturing fast-moving objects at low frame rates results in blurry images. These deficiencies significantly degrade human observation and machine processing. Fortunately, the two information sources are inherently complementary - events with microsecond-level temporal resolution, which are triggered by the edges of objects recorded in a latent sharp image, can supply rich motion details missing from the blurry one. In this work, we bring the two types of data together and introduce a simple yet effective unifying algorithm to jointly reconstruct blur-free images and noise-robust events in an iterative coarse-to-fine fashion. Specifically, an event-regularized prior offers precise high-frequency structures and dynamic features for blind deblurring, while image gradients serve as a kind of faithful supervision in regulating neuromorphic noise removal. Comprehensively evaluated on real and synthetic samples, such a synergy delivers superior reconstruction quality for both images with severe motion blur and raw event streams with a storm of noise, and also exhibits greater robustness to challenging realistic scenarios such as varying levels of illumination, contrast and motion magnitude. Meanwhile, it can be driven by much fewer events and holds a competitive edge at computational time overhead, rendering itself preferable as available computing resources are limited. Our solution gives impetus to the improvement of both sensing data and paves the way for highly accurate neuromorphic reasoning and analysis.

Modified Thermodilution for Simultaneous Cardiac Output and Recirculation Assessment in Veno-venous Extracorporeal Membrane Oxygenation: A Prospective Diagnostic Accuracy Study.

Thermodilution is unreliable in veno-venous extracorporeal membrane oxygenation (VV-ECMO). Systemic oxygenation depends on recirculation fractions and ratios of extracorporeal membrane oxygenation (ECMO) flow to cardiac output. In a prospective in vitro simulation, this study assessed the diagnostic accuracy of a modified thermodilution technique for recirculation and cardiac output. The hypothesis was that this method provided clinically acceptable precision and accuracy for cardiac output and recirculation. Two ECMO circuits ran in parallel: one representing a VV-ECMO and the second representing native heart, lung, and circulation. Both circuits shared the right atrium. Extra limbs for recirculation and pulmonary shunt were added. This study simulated ECMO flows from 1 to 2.5 l/min and cardiac outputs from 2.5 to 3.5 l/min with recirculation fractions (0 to 80%) and pulmonary shunts. Thermistors in both ECMO limbs and the pulmonary artery measured the temperature changes induced by cold bolus injections into the arterial ECMO limb. Recirculation fractions were calculated from the ratio of the areas under the temperature curve (AUCs) in the ECMO limbs and from partitioning of the bolus volume (flow based). With known partitioning of bolus volumes between ECMO and pulmonary artery, cardiac output was calculated. High-precision ultrasonic flow probes served as reference for Bland-Altman plots and linear mixed-effect models. Accuracy and precision for both the recirculation fraction based on AUC (bias, -5.4%; limits of agreement, -18.6 to 7.9%) and flow based (bias, -5.9%; limits of agreement, -18.8 to 7.0%) are clinically acceptable. Calculated cardiac output for all recirculation fractions was accurate but imprecise (RecirculationAUC: bias 0.56 l/min; limits of agreement, -2.27 to 3.4 l/min; and RecirculationFLOW: bias 0.48 l/min; limits of agreement, -2.22 to 3.19 l/min). Recirculation fraction increased bias and decreased precision. Adapted thermodilution for VV-ECMO allows simultaneous measurement of recirculation fraction and cardiac output and may help optimize patient management with severe respiratory failure.

Homogeneous fluorescence immunoassay based on AuNPs quenching dendritic silica assembled with multicolor QDs for the simultaneous determination of four mycotoxins in cereals

In this work, a novel homogeneous fluorescence immunoassay for the simultaneous detection of ochratoxin A (OTA), aflatoxin B1 (AFB1), fumonisins B1 (FB1), and zearalenone (ZEN) in cereal samples is established. Here, amino groups and thiol groups are grafted onto the surface of dendritic mesoporous silica nanoparticles (DMSNs) to improve loading rate and fluorescence preservation rate of quantum dots (QDs), enabling enrichment and amplification of fluorescence signals. In this test system, the fluorescence of the signal probe (DMSNs@QDs@Ab) is quenched by AuNPs, and then the presence of the targets makes the fluorescence of the signal probes recover. The correlation between the recovered fluorescence value and mycotoxin concentration is applied to achieve quantitative detecting mycotoxins. This assay displays good linear correlation in range of 0.01–100 μg L-1, with the limit of detection (LOD) of 0.0001 μg L-1 for OTA, 0.0008 μg L-1 for AFB1, 0.001 μg L-1 for FB1, and 0.0006 μg L-1 for ZEN, respectively. The proposed assay is applied to detect multiple mycotoxins in contaminated cereal samples and the test results is confirmed by liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Avail of silica with high loading capacity for assembling multicolor QDs to achieve simultaneous multi-signal output, a simple and efficient homogeneous fluorescence immunoassay is proposed for the first time. This multiplex test strategy provides a good idea for simultaneous, rapid, and sensitive detection of multiple hazardous substances in food and environment samples.

A novel sensitive fluorescent probe with double channels for highly effective recognition of biothiols

Biothiols play a crucial role in maintaining redox balance in organisms, and anomalous levels of biothiols in human organs can lead to various sicknesses and biological disorders. This work developed a novel sensitive fluorescent probe TZ-NBD with double channels for highly efficient recognition of biothiols. TZ-NBD adopts 4-Chloro-7-nitrobenzofurazan (NBD-Cl) as the recognition moiety with simultaneous fluorescence output. By incorporating NBD-Cl with the other fluorophore, benzothiazole dihydrocyclopentachromene derivative (TZ-OH), the dual-channel sensitive fluorescence probe TZ-NBD was built. The existence of Cys/ Hcy could significantly trigger both the green and red fluorescent emissions, which were derived from fluorophores amine-substituted NBD and TZ-OH, respectively. While exposing to GSH, only the red-channel fluorescence signal could be detected, indicating the release of TZ-OH. The phenomena was mainly attributed to the fact that sulfur-substituted NBD has nearly no fluorescence, while amine-substituted NBD shows obvious green fluorescence. In our study, TZ-NBD exhibited dual-channel sensitivity, fast response, and excellent selectivity to biothiols in vitro. Moreover, TZ-NBD was favorably utilized for recognition of biothiols in vivo. We believe that the sensitive fluorescence probe with double channels can afford an alternate approach for monitoring biothiols in organisms and would be useful for studying diseases associated with biothiols.