A Co3O4 Nanoparticle-Modified Screen-Printed Electrode Sensor for the Detection of Nitrate Ions in Aquaponic Systems.

In this study, a screen-printed electrode (SPE) modified with cobalt oxide nanoparticles (Co3O4 NPs) was used to create an all-solid-state ion-selective electrode used as a potentiometric ion sensor for determining nitrate ion (NO3-) concentrations in aquaculture water. The effects of the Co3O4 NPs on the characterization parameters of the solid-contact nitrate ion-selective electrodes (SC-NO3--ISEs) were investigated. The morphology, physical properties and analytical performance of the proposed NO3--ion selective membrane (ISM)/Co3O4 NPs/SPEs were studied by X-ray diffraction (XRD), energy-dispersive spectroscopy (EDS), transmission electron microscopy (TEM), scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), potentiometric measurements, and potentiometric water layer tests. Once all conditions were optimized, it was confirmed that the screen-printed electrochemical sensor had high potential stability, anti-interference performance, good reproducibility, and no water layer formation between the selective membrane and the working electrode. The developed NO3--ISM/Co3O4 NPs/SPE showed a Nernstian slope of -56.78 mV/decade for NO3- detection with a wide range of 10-7-10-2 M and a quick response time of 5.7 s. The sensors were successfully used to measure NO3- concentrations in aquaculture water. Therefore, the electrodes have potential for use in aquaponic nutrient solution applications with precise detection of NO3- in a complicated matrix and can easily be used to monitor other ions in aquaculture water.

A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information.

To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13-6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71-100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83-0.88, and the RAD value was 8.05-9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards.

Generation of linear frequency-modulated signals with improved time-bandwidth product based on an optical frequency comb.

A generation scheme for the linear frequency-modulated (LFM) signals with tunable carrier frequency and improved time-bandwidth product (TBWP) using an optical frequency comb (OFC) and a fiber Bragg grating (FBG) time delay line is proposed and demonstrated by simulation. In the scheme, intensity modulation is used to convert the continuous time-domain waveform to an optical pulse with a duty cycle of one-sixteenth, and two cascaded polarization modulators driven by two different radio frequency signals are used to generate 16-line OFC without an optical filter. Then the 16-line OFC is injected into the FBG time delay line, which consists of 16 discrete FBGs, and each spectral line is reflected by the specific FBG after different time delays. By properly designing the position of the FBGs, the 16 spectral lines of the 16-line OFC are separated in the time domain. The time-separated OFC is then modulated by a phase modulator driven by a periodic parabolic signal for introducing parabolic phase modulation. Subsequently, after the heterodyne beating between the phase-modulated optical signal and the local oscillator light wave emitted from a tunable laser source (TLS) in a photodiode, an electrical LFM signal with improved TBWP is generated. Simulation results show that the generated LFM signal has the bandwidth of 31.78 GHz and TBWP of 1365.33, and its center frequency can be tuned by varying the wavelength of the TLS.

Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants.

Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.

Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging.

Measurement of plant nitrogen (N), phosphorus (P), and potassium (K) levels are important for determining precise fertilization management approaches for crops cultivated in greenhouses. To accurately, rapidly, stably, and nondestructively measure the NPK levels in tomato plants, a nondestructive determination method based on multispectral three-dimensional (3D) imaging was proposed. Multiview RGB-D images and multispectral images were synchronously collected, and the plant multispectral reflectance was registered to the depth coordinates according to Fourier transform principles. Based on the Kinect sensor pose estimation and self-calibration, the unified transformation of the multiview point cloud coordinate system was realized. Finally, the iterative closest point (ICP) algorithm was used for the precise registration of multiview point clouds and the reconstruction of plant multispectral 3D point cloud models. Using the normalized grayscale similarity coefficient, the degree of spectral overlap, and the Hausdorff distance set, the accuracy of the reconstructed multispectral 3D point clouds was quantitatively evaluated, the average value was 0.9116, 0.9343 and 0.41 cm, respectively. The results indicated that the multispectral reflectance could be registered to the Kinect depth coordinates accurately based on the Fourier transform principles, the reconstruction accuracy of the multispectral 3D point cloud model met the model reconstruction needs of tomato plants. Using back-propagation artificial neural network (BPANN), support vector machine regression (SVMR), and gaussian process regression (GPR) methods, determination models for the NPK contents in tomato plants based on the reflectance characteristics of plant multispectral 3D point cloud models were separately constructed. The relative error (RE) of the N content by BPANN, SVMR and GPR prediction models were 2.27%, 7.46% and 4.03%, respectively. The RE of the P content by BPANN, SVMR and GPR prediction models were 3.32%, 8.92% and 8.41%, respectively. The RE of the K content by BPANN, SVMR and GPR prediction models were 3.27%, 5.73% and 3.32%, respectively. These models provided highly efficient and accurate measurements of the NPK contents in tomato plants. The NPK contents determination performance of these models were more stable than those of single-view models.

Evaluation of Object Surface Edge Profiles Detected with a 2-D Laser Scanning Sensor.

Canopy edge profile detection is a critical component of plant recognition in variable-rate spray control systems. The accuracy of a high-speed 270° radial laser sensor was evaluated in detecting the surface edge profiles of six complex-shaped objects. These objects were toy balls with a pink smooth surface, light brown rectangular cardboard boxes, black and red texture surfaced basketballs, white smooth cylinders, and two different sized artificial plants. Evaluations included reconstructed three-dimensional (3-D) images for the object surfaces with the data acquired from the laser sensor at four different detection heights (0.25, 0.50, 0.75, and 1.00 m) above each object, five sensor travel speeds (1.6, 2.4, 3.2, 4.0, and 4.8 km h-1), and 8 to 15 horizontal distances to the sensor ranging from 0 to 3.5 m. Edge profiles of the six objects detected with the laser sensor were compared with images taken with a digital camera. The edge similarity score (ESS) was significantly affected by the horizontal distances of the objects, and the influence became weaker when the objects were placed closer to each other. The detection heights and travel speeds also influenced the ESS slightly. The overall average ESS ranged from 0.38 to 0.95 for all the objects under all the test conditions, thereby providing baseline information for the integration of the laser sensor into future development of greenhouse variable-rate spray systems to improve pesticide, irrigation, and nutrition application efficiencies through watering booms.

[Preparation and evaluation of doxorubicin hydrochloride liposomes modified by poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate].

In this study, the buffering capacity of amphiphilic pH-sensitivity copolymer poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (PEOZ-CHMC) was evaluated. The ammonium sulfate gradient method was used to prepare doxorubicin hydrochloride (DOX x HCl)-loaded liposomes (DOX-L), and then the post-insertion method was used to prepare PEOZ-CHMC and polyethylene glycol-distearoyl phosphatidyl ethanolamine (PEG-DSPE) modified DOX x HCl-loaded liposomes (PEOZ-DOX-L and PEG-DOX-L). The physico-chemical properties, in vitro drugs release behavior, cellular toxicity and intracellular delivery of liposomes were evaluated, separately. The results showed that PEOZ-CHMC has a satisfactory buffering capacity. The sephadex G-50 column centrifugation method and dynamic light scattering were used to determine the encapsulation efficiency (EE) and particle size of liposomes. The EE and particle size of DOX-L were (97.3 ± 1.4) % and 120 nm, respectively, and the addition of PEOZ-CHMC or PEG-DSPE had no influence on EE and particle size. The zeta potentials of three kinds of liposomes were negative. The release behavior of various DOX liposomes in vitro was investigated by dialysis method. In phosphate buffer solution (PBS) at pH 7.4, DOX x HCl was released from PEOZ-DOX-L in a sustained manner. While in PBS at pH 5.0, the release rate of DOX x HCl from PEOZ-DOX-L increased significantly, which suggested DOX x HCl was released from PEOZ-DOX-L in a pH-dependent manner. The intracellular delivery of liposomes was investigated by confocal laser scanning microscopy (CLSM). The CLSM images indicated that PEOZ-DOX-L showed efficient intracellular trafficking including endosomal escape and release DOX x HCl into nucleus, as well as the DOX-L and PEG-DOX-L had no this effect. The cytotoxicity of liposomes against MCF-7 cells was detected by using MTT assay. The results showed that antiproliferative effects of PEOZ-DOX-L enhanced with pH value decreased, whereas DOX-L and PEG-DOX-L did not have any significant difference in inhibitions at different pH conditions. Therefore, the problems of the inhibition of cellular uptake of liposomes and the failed endosomal escape of pH-sensitive liposomes by PEG chain can be overcome by the pH-sensitive liposomes constructed by PEOZ-CHMC.

Nondestructive detection of SSC in multiple pear (Pyrus pyrifolia Nakai) cultivars using Vis-NIR spectroscopy coupled with the Grad-CAM method.

Vis-NIR spectroscopy coupled with chemometric models is frequently used for pear soluble solid content (SSC) prediction. However, the model robustness is challenged by the variations in pear cultivars. This study explored the feasibility of developing universal models for predicting SSC of multiple pear varieties to improve the model's generalizability. The mature fruits of 6 pear cultivars with green skin (Pyrus pyrifolia Nakai cv. 'Cuiyu', 'Sucui No.1' and 'Cuiguan') and brown skin (Pyrus pyrifolia Nakai cv. 'Hosui','Syusui' and 'Wakahikari') were used to establish single-cultivar models and multi-cultivar universal models using convolutional neural network (CNN), partial least square (PLS), and support vector regression (SVR) approaches. Multi-cultivar universal models were built using full spectra and important variables extracted by gradient-weighted class activation mapping (Grad-CAM), respectively. The universal models based on important variables obtained satisfactory performances with RMSEPs of 0.76, 0.59, 0.80, 1.64, 0.98, and 1.03°Brix on 6 cultivars, respectively.

Silver nanostars arrayed on GO/MWCNT composite membranes for enrichment and SERS detection of polystyrene nanoplastics in water.

Nanoplastic water contamination has become a critical environmental issue, highlighting the need for rapid and sensitive detection of nanoplastics. In this study, we aimed to prepare a graphene oxide (GO)/multiwalled carbon nanotube (MWCNT)-silver nanostar (AgNS) multifunctional membrane using a simple vacuum filtration method for the enrichment and surface-enhanced Raman spectroscopy (SERS) detection of polystyrene (PS) nanoplastics in water. AgNSs, selected for the size and shape of nanoplastics, have numerous exposed Raman hotspots on their surface, which exert a strong electromagnetic enhancement effect. AgNSs were filter-arrayed on GO/MWCNT composite membranes with excellent enrichment ability and chemical enhancement effects, resulting in the high sensitivity of GO/MWCNT-AgNS membranes. When the water samples flowed through the portable filtration device with GO/MWCNT-AgNS membranes, PS nanoplastics could be effectively enriched, and the retention rate for 50 nm PS nanoplastics was 97.1 %. Utilizing the strong SERS effect of the GO/MWCNT-AgNS membrane, we successfully detected PS nanoparticles with particle size in the range of 50-1000 nm and a minimum detection concentration of 5 × 10-5 mg/mL. In addition, we detected 50, 100, and 200 nm PS nanoplastics at concentrations as low as 5 × 10-5 mg/mL in real water samples using spiking experiments. These results indicate that the GO/MWCNT-AgNS membranes paired with a portable filtration device and Raman spectrometer can effectively enrich and rapidly detect PS nanoplastics in water, which has great potential for on-site sensitive water quality safety evaluation.

Automatic pest identification system in the greenhouse based on deep learning and machine vision.

Monitoring and understanding pest population dynamics is essential to greenhouse management for effectively preventing infestations and crop diseases. Image-based pest recognition approaches demonstrate the potential for real-time pest monitoring. However, the pest detection models are challenged by the tiny pest scale and complex image background. Therefore, high-quality image datasets and reliable pest detection models are required. In this study, we developed a trapping system with yellow sticky paper and LED light for automatic pest image collection, and proposed an improved YOLOv5 model with copy-pasting data augmentation for pest recognition. We evaluated the system in cherry tomato and strawberry greenhouses during 40 days of continuous monitoring. Six diverse pests, including tobacco whiteflies, leaf miners, aphids, fruit flies, thrips, and houseflies, are observed in the experiment. The results indicated that the proposed improved YOLOv5 model obtained an average recognition accuracy of 96% and demonstrated superiority in identification of nearby pests over the original YOLOv5 model. Furthermore, the two greenhouses show different pest numbers and populations dynamics, where the number of pests in the cherry tomato greenhouse was approximately 1.7 times that in the strawberry greenhouse. The developed time-series pest-monitoring system could provide insights for pest control and further applied to other greenhouses.

Influence of maturity on bruise detection of peach by structured multispectral imaging.

Peaches are easily bruising during all stages of postharvest handling, maturity can affect the characteristics and detection of bruising, which is directly related to the quality and shelf life of peach. The main objective of this research was to investigate the effect of maturity on the early detection of postharvest bruising in peach based on structured multispectral imaging (S-MSI) system. The S-MSI data was measured for bruised peaches, followed by microstructural (CLSM), and biochemical (oxidative browning-related enzyme activities, gene expression, and phenolic compound metabolism) measurements. As the maturity increases, the external impact stress could further induce the accumulation of phenolics through the phenylpropane pathway and pulp oxidative browning, resulting in more pronounced external damage; and the spectral reflectance value of bruised peach was getting smaller, and the spectral waveform gradually flattened out. Three characteristic bands of 781, 824, 867 nm were selected from structured spectra (669-955 nm) related to bruising. The watershed algorithm was adopted for bruise detection, the detection rates for bruised peaches based on three maturity levels (S1-S3) were 91-92%, 90.71-97.43%, and 97.14-99.86%, respectively. This research demonstrated that S-MSI system coupled with watershed algorithm, can enhance our capability of detecting the early bruised peaches of different maturity levels.

In-situ SERS detection of quinolone antibiotic residues in aquaculture water by multifunctional Fe3O4@mTiO2@Ag nanoparticles.

Antibiotic residues in aquaculture environments disrupt the ecosystem balance and pose a potential hazard to human health when entering the food chain. Therefore, ultra-sensitive detection of antibiotics is necessary. In this study, a multifunctional Fe3O4@mTiO2@Ag core-shell nanoparticle (NP), synthesized using a layer-by-layer method, was demonstrated to be useful as an enhanced substrate for in-situ surface-enhanced Raman spectroscopy (SERS) detection of various quinolone antibiotics in aqueous environments. The results showed that the minimum detectable concentrations of the six investigated antibiotics were 1 × 10-9 mol/L (ciprofloxacin, danofloxacin, enoxacin, enrofloxacin, and norfloxacin) and 1 × 10-8 mol/L (difloxacin hydrochloride) under the enrichment and enhancement of Fe3O4@mTiO2@Ag NPs. Additionally, there was a good quantitative relationship between the antibiotics concentrations and SERS peak intensities within a certain detection range. The results of the spiked assay of actual aquaculture water samples showed that the recoveries of the six antibiotics ranged from 82.9% to 113.5%, with relative standard deviations ranging from 1.71% to 7.24%. In addition, Fe3O4@mTiO2@Ag NPs achieved satisfactory results in assisting the photocatalytic degradation of antibiotics in aqueous environments. This provides a multifunctional solution for low concentration detection and efficient degradation of antibiotics in aquaculture water.

Low-pressure ultraviolet-H2O2 photolysis for restoring the anodic stripping voltammetry signal: a new strategy for the detection of heavy metal ions in complex organic matter.

A new strategy based on low-pressure ultraviolet (LPUV)-H2O2 advanced oxidation photolysis for the quantitative determination of organic heavy metal ions (HMIs) in soil was proposed for the efficient, low-cost, accurate, and green detection of Pb(II) and Cd(II) in soil extracts by breaking the complexation of HMIs and organic matters, consequently restoring the ASV signals of target HMIs. The key parameters of the proposed LPUV-H2O2 photolysis system for the restoration of stripping responses were optimized; the conversion of organic matter to inorganic matter during the photolysis was investigated by total organic carbon (TOC); the degradation kinetics of humic acid sodium (HAS) was measured by UV-vis spectroscopy (UV); the pathway of HAS converted to small molecule organics during degradation was observed by fluorescence spectroscopy (FS); additionally, Fourier transform infrared spectroscopy (FTIR) was used to study the complexation between HAS and HMIs. The results showed that the stripping signals of target HMIs in the simulated soil samples can be restored to nearly 100% with a good repeatability, and the restoration ratio of the stripping signal fluctuated within 10%. And the feasibility of the proposed method for the accurate detection of HMIs in the real soil samples was verified; the results showed that 93.7% of Cd(II) and 92.5% of Pb(II) in real soil extracts were detectable.

A flexible and wearable PET-based chemiresistive H2S gas sensor modified with MoS2-AgCl@AgNPs nanocomposite for the dynamic monitoring of egg spoilage.

In this study, a flexible and wearable chemiresistive hydrogen sulfide (H2S) sensor is developed by modifying the MoS2-AgCl@AgNPs (MAAN) nanocomposite on a flexible PET-based Au interdigital electrode (FPAIDE) (MAAN/FPAIDE) to monitor egg spoilage at room temperature inexpensively. A new method is developed for the low-cost batch fabrication of MAAN/FPAIDEs by laser direct writing. The morphology and composition of the synthesized MAAN nanocomposite are investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and transmission electron microscopy (TEM). Based on the oxygen adsorption model, a new H2S sensing mechanism is discussed, which is related to the formation of p-n junctions between MoS2 and AgCl and the specific adsorption of H2S by AgNPs on the MAAN sensing layer, causing a decrease in resistance. X-ray photoelectron spectroscopy (XPS) is used to characterize the charge transfer between gas molecules and the MAAN sensing layer and sulfide generation during the response process. The concentration of H2S can be detected down to 27 ppb at 25 °C. Finally, the prepared sensor has been successfully utilized in the real-time monitoring of egg spoilage with satisfactory results, indicating its great potential for the application of fresh food quality and safety supervision and the smart packaging of poultry eggs.

A flexible and wearable paper-based chemiresistive sensor modified with SWCNTs-PdNPs-polystyrene microspheres composite for the sensitive detection of ethylene gas: A new method for the determination of fruit ripeness and corruption.

This study developed a flexible and wearable paper-based chemoresistive sensor (FWPCS) by modifying a SWCNT-PdNP-polystyrene microsphere (SPPM) composite (SPPM/FWPCS) for the low-cost and online determination of fruit ripeness and corruption. A new method for the batch and low-cost fabrication of SPPM/FWPCSs based on laser direct writing was proposed. The sensing mechanism of FWPCS relies on the electron depletion layer in the sensing composite created by the Schottky barriers among SWCNTs, PdNPs, and the adsorbed oxygen, along with the construction of O2-. When the SPPM sensing film is exposed to ethylene, trapped electrons are released into the conduction band through oxidation and cleavage of ethylene, causing a decrease in resistance. The properties and morphology of the synthesized SPPM composite were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy. Additionally, the key parameters for the fabrication of SPPMs/FWPCS related to the sensing performance were optimized. The concentration of C2H4 can be detected down to 100 ppb using the SPPMs/FWPCS at 25 °C. Finally, the real-time determination of banana ripeness and corruption verified the feasibility of the sensor, indicating that the SPPMs/FWPCS has prospects in monitoring fruit ripeness and corruption during storage and transportation.

An efficient pretreatment method based on AgNPs-doped SnO2 photocatalyst for the accurate detection of heavy metals in organic-rich water samples.

Humic acid (HA), the primary composition of natural organic matter (NOM) widely distributed in water and soil, can complex with heavy metal ions (HMIs), i.e., Cd(II) and Pb(II) in this study, which deters the accurate detection of HMIs using square wave anodic stripping voltammetry (SWASV). Hence, in this study, an efficient pretreatment method was proposed to restore the electrochemical signal of Cd(II) and Pb(II) by breaking the complexation based on AgNPs-doped SnO2 photocatalyst combined with LP/UV irradiation. Optimization of the key parameters for electrochemical signal restoration including pH for photolysis, AgNPs doping rate, photocatalyst dosage and photolysis time were performed to further elevating the accuracy in the proposed pretreatment method over 96.9% for Cd(II) and Pb(II) in 15 min. The effect of different HA concentrations on SWASV signal of Cd(II) and Pb(II) was also investigated adopting the optimal parameters. Then, the UV-vis absorption spectra, crystal structure, and the morphology of AgNPs-doped SnO2 photocatalyst were investigated to excavate the reasons behind the most excellent AgNPs doping rate to SnO2 in signal restoration. Moreover, the behavior of HA degradation and transformation under LP/UV irradiation was studied to investigate the mechanism of electrochemical signal restoration. Finally, the feasibility of the proposed method was testified by comparing detection results with ICP-MS results using real water samples extracted from aquaculture water.

SERS Determination of Trace Phosphate in Aquaculture Water Based on a Rhodamine 6G Molecular Probe Association Reaction.

Although phosphate (Pi) is a necessary nutrient for the growth of aquatic organisms, the presence of excess Pi leads to water eutrophication; thus, it is necessary to accurately determine the content of Pi in water. A method for the determination of trace Pi in aquaculture water was developed based on surface-enhanced Raman spectroscopy (SERS) combined with rhodamine 6G (R6G)-modified silver nanoparticles (AgNPs) as the active substrate. The adsorption of R6G on the AgNP surfaces led to a strong SERS signal. However, in the presence of Pi and ammonium molybdate, phosphomolybdic acid formed, which further associated with R6G to form a stable R6G-PMo12O403- association complex, thereby hindering the adsorption of R6G on the AgNPs, and reducing the SERS intensity; this sequence formed the basis of Pi detection. The decrease in the SERS intensity was linear with respect to the Pi concentration (0.2-20 µM), and the limit of detection was 29.3 nM. Upon the application of this method to the determination of Pi in aquaculture water, a recovery of 94.4-107.2% was obtained (RSD 1.77-6.18%). This study provides an accurate, rapid, and sensitive method for the trace determination of Pi in aquaculture water, which is suitable for on-site detection.

Innovation of strip fertilization planting for rice straw crushing with back-throwing and interrow-laying.

BACKGROUND: In order to improve the current situations in rice-wheat rotation region in the middle and lower reaches of the Yangtze River, such as large amount of rice straw, complex returning process, short-time stubble connection, high power consumption, poor smoothness and especially unstable performance, and further promote the resource utilization process of full straw returning in Jiangsu province, this study, combined with the agronomic requirements of wheat sowing in rice-stubble land, developed an innovation of strip fertilization planting for straw crushing with back-throwing and interrow-laying in full stubble fields. RESULTS: Structural design and theoretical analysis were carried out on key components such as straw crushing device, broken-straw control device, soil rotary-tillage device and power transmission device, etc., to determine the corresponding structure and operating performance parameters, and then the field performance and verification tests were completed on the uniformity of inter-row mulching-straw Y1 and the variability of seed-band width Y2. The results showed that the crushing spindle rotation-speed A had an extremely significant impact on Y1, followed by the machine ground speed B. The conveying impeller rotation-speed C had an extremely significant effect on Y2, also secondary to the machine ground speed B. And the superior combination of factor levels as A2B2C2 was adopted through the comprehensive power energy consumption analysis. The verification test results indicated that under the optimized operation parameter combination, namely, when the crushing spindle rotation-speed A was 2100 r/min, the machine ground speed B was 0.8 m/s, and the conveying impeller rotation-speed C was 210 r/min, the mean value of inter-row straw uniformity Y1 and seed-band width variation Y2 were 90.85% and 10.73%, respectively, after machine operation. CONCLUSION: It meets the requirements of operation quality and planting agronomy of relevant protective tillage machinery, and provides technical and equipment support for the research and development of similar straw crushing and no-tillage sowing.

A flexible and disposable electrochemical sensor for the evaluation of arsenic levels: A new and efficient method for the batch fabrication of chemically modified electrodes.

In this study, a novel flexible and disposable electrochemical sensor was fabricated through the straightforward laser-induced synthesis of catalytic Au nanoparticles (AuNPs)@LIG composite for the square wave stripping voltammetry (SWASV) determination of arsenic in soil. In this process, polyimide and a metal precursor (PI-MP) were transformed to three-dimensional porous LIG and AuNPs through the graphitization, photoreduction and thermal reduction of laser ablation. To our knowledge, this is the first time that laser-induced PI-MP was employed to fabricate chemically modified electrodes on a large scale for arsenic determination. X-ray Photoelectron Spectroscopy (XPS), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectrometer (EDX), X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM) and N2 adsorption isotherm were used to characterize the morphological and physical properties of the materials, which demonstrated the successful synthesis of AuNPs@LIG nanocomposite. Additionally, the mechanism of AuNPs@LIG synthesis by laser induction was also investigated, and the deposition potential and the deposition time for the SWASV determination of arsenic were also optimized. The electrochemical sensor fabricated using the proposed method shows satisfactory sensitivity, stability and reliability and exhibits a good linear relationship over concentrations ranging from 0 to 40 µg/L, with a low determination limit of 0.18 µg/L. Furthermore, the obtained AuNPs@LIG surface exhibited good conductivity with a charge transfer resistance (Rct) of 73.85 Ω and a large real surface area of 807.28 mm2. Finally, several real soil samples were analyzed using the prepared sensor with a satisfactory result, which verified the feasibility of this sensor. The results show that the method for chemically modified electrode fabrication proposed in this paper has good commercial potential for the batch manufacturing of electrochemical sensors.

Evaluation of the Changes in Optical Properties of Peaches with Different Maturity Levels during Bruising.

The main objective was to measure the optical coefficients of peaches after bruising at different maturity levels and detect bruises. A spatially resolved method was used to acquire absorption coefficient (µa) and the reduced scattering coefficient (µs') spectra from 550 to 1000 nm, and a total of 12 groups (3 maturity levels * 4 detection times) were used to assess changes in µa and µs' resulting from bruising. Maturation and bruising both caused a decrease in µs' and an increase in µa, and the optical properties of immature peaches changed more after bruising than the optical properties of ripe peaches. Four hours after bruising, the optical properties of most samples were significantly different from those of intact peaches (p < 0.05), and the optical properties showed damage to tissue earlier than the appearance symptoms observed with the naked eye. The classification results of the Support Vector Machine model for bruised peaches showed that µa had the best classification accuracy compared to µs' and their combinations (µa × µs', µeff). Overall, based on µa, the average detection accuracies for peaches after bruising of 0 h, 4 h, and 24 h were increased.