Table_1_Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying.DOCX

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.</p

Effect of Surface Wettability Properties on the Electrical Properties of Printed Carbon Nanotube Thin-Film Transistors on SiO₂/Si Substrates

The precise placement and efficient deposition of semiconducting single-walled carbon nanotubes (sc-SWCNTs) on substrates are challenges for achieving printed high-performance SWCNT thin-film transistors (TFTs) with independent gates. It was found that the wettability of the substrate played a key role in the electrical properties of TFTs for sc-SWCNTs sorted by poly­[(9,9-dioctylfluorene-2,7-diyl)-<i>co</i>-(1,4-benzo-2,1,3-thiadiazole)] (PFO-BT). In the present work we report a simple and scalable method which can rapidly and selectively deposit a high concentration of sc-SWCNTs in TFT channels by aerosol-jet-printing. The method is based on oxygen plasma treatment of substrates, which tunes the surface wettability. TFTs printed on the treated substrates demonstrated a low operation voltage, small hysteresis, high mobility up to 32.3 cm² V^–1 s^–1, and high on/off ratio up to 10⁶ after only two printings. Their mobilities were 10 and 30 times higher than those of TFTs fabricated on untreated and low-wettability substrates. The uniformity of printed TFTs was also greatly improved. Inverters were constructed by printed top-gate TFTs, and a maximum voltage gain of 17 at <i>V</i>_dd = 5 V was achieved. The mechanism of such improvements is that the PFO-BT-functionalized sc-SWCNTs are preferably immobilized on the oxygen plasma treated substrates due to the strong hydrogen bonds between sc-SWCNTs and hydroxyl groups on the substrates

Image_2_Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying.TIF

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.</p

Image_1_Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying.TIF

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.</p

Optoelectronic Properties of Printed Photogating Carbon Nanotube Thin Film Transistors and Their Application for Light-Stimulated Neuromorphic Devices

Artificial synapses/neurons based on electronic/ionic hybrid devices have attracted wide attention for brain-inspired neuromorphic systems since it is possible to overcome the von Neumann bottleneck of the neuromorphic computing paradigm. Here, we report a novel photoneuromorphic device based on printed photogating single-walled carbon nanotube (SWCNT) thin film transistors (TFTs) using lightly n-doped Si as the gate electrode. The drain currents of the printed SWCNT TFTs can gradually increase to over 3000 times of their starting value after being pulsed with light stimulation, and the electrical signals can maintain for over 10 min. These characteristics are similar to the learning and memory functions of brain-inspired neuromorphic systems. The working mechanism of the light-stimulated neuromorphic devices is investigated and described here in detail. Important synaptic characteristics, such as low-pass filtering characteristics and nonvolatile memory ability, are successfully emulated in the printed light-stimulated artificial synapses. It demonstrates that the printed SWCNT TFT photoneuromorphic devices can act as the nonvolatile memory units and perform photoneuromorphic computing, which exhibits potential for future neuromorphic system applications