The development of broadband photosensors has become crucial in various fields. Indium-gallium-zinc oxide (IGZO, In:Ga:Zn = 1:1:1) phototransistors with PbS quantum dots (QDs) have shown promising features for such sensors, such as reasonable mobility, low leakage current, good photosensitivity, and low-cost fabrication. However, the instability of PbS QD/IGZO phototransistors under an air atmosphere and prolonged storage remain serious concerns. In this article, two concepts to improve the reliability of PbS QD/IGZO phototransistors were implemented. P-type doping in the PbS QD layer through oxidation allows increasing the built-in potential between IGZO and PbS QDs, leading to enhancement in photoinduced electron-hole pair creation. Second, agglomeration and fusion of a PbS QDs layer were controlled via thermal annealing, which facilitated the transport of photocreated carriers. The p-type doping and interconnection of a PbS QD layer can be achieved by deposition and subsequent thermal annealing of gallium oxide (Ga2O3) on PbS QD/IGZO stacks. The resulting Ga2O3/PbS QD/IGZO phototransistors exhibited high-performance switching characteristics under dark conditions. Notably, they showed a remarkable photoresponsivity of 196.69 ± 4.05 A/W and a detectivity of (5.47 ± 1.4) × 1012 Jones even at a long-wavelength illumination of 1550 nm. While the unpassivated PbS/IGZO phototransistor suffered serious degradation in optical performance after 2 weeks of storage, the Ga2O3/PbS QD/IGZO phototransistor demonstrated enhanced stability, maintaining high performance for over 5 weeks. These findings suggest that Ga2O3/PbS QD/IGZO phototransistors offer a feasible approach for the fabrication of large-scale active matrix broadband photosensor arrays, potentially revolutionizing optical sensing in various cutting-edge applications.

This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.

Nitrogen nitrates play a significant role in the soil\'s nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate-nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.

Applications of single-walled carbon nanotubes (SWCNTs) in bioimaging and biosensing have been limited by difficulties with isolating single-chirality nanotube preparations with desired functionalities. Unique optical properties, such as multiple narrow near-infrared bands and several modes of signal transduction, including solvatochromism and FRET, are ideal for live cell/organism imaging and sensing applications. However, internanotube FRET has not been investigated in biological contexts. We developed single-chirality subcellular SWCNT imaging probes and investigated their internanotube FRET capabilities in live cells. To functionalize SWCNTs, we replaced the surfactant coating of aqueous two-phase extraction-sorted single-chirality nanotubes with helical polycarbodiimide polymers containing different functionalities. We achieved single-chirality SWCNT targeting of different subcellular structures, including the nucleus, to enable multiplexed imaging. We also targeted purified (6,5) and (7,6) chiralities to the same structures and observed internanotube FRET within these organelles. This work portends the use of single-chirality carbon nanotube optical probes for applications in biomedical research.

Non-destructive monitoring of chick embryonic growth can provide vital management insights for poultry farmers and other stakeholders. Although non-destructive studies on fertility, hatching time and gender have been conducted recently, there has been no available method for embryonic growth observation, especially during the second half of incubation. Therefore, this work investigated the feasibility of using near-infrared (NIR) sensor-based egg opacity values-the amount of light lost when passing through the egg-for indirectly observing embryo growth during incubation. ROSS 308 eggs were selected based on size, mass and shell color for this experiment. To estimate the embryo size precisely, we fit various mathematical growth functions during incubation, based on the opacity value of incubated eggs. Although all the growth models tested performed similarly in fitting the data, the exponential and power functions had better performances in terms of co-efficient of determination (0.991 and 0.994 respectively) and RMSE to explain embryo growth during incubation. From these results, we conclude that the modeling paradigm adopted provides a simple tool to non-invasively investigate embryo growth. These models could be applied to resolving developmental biology, embryonic pathology, industrial and animal welfare issues in the near future.

Plastic debris are ubiquitous in soil and bring severe threatening to environment and ecosystem. It is of great significance to extensively investigate the plastic pollution level in soil. An ultra-portable Near-infrared (NIR) sensor was used to detect plastic pollution level in soil. Soil samples were collected from three different regions and artificially polluted in two degrees (10-1.5% and 0.7-0.15%). Here, instead of constructing detection models for specific soil region, transfer learning approaches were explored to build classification model which could evaluate plastic pollution level in different soil regions simultaneously. The transfer learning algorithms, Manifold Embedded Distribution Alignment (MEDA) and Transfer Component Analysis (TCA), were employed for transfer learning model construction. Supporting Vector Machine (SVM) models were calibrated for transferability analysis and comparison. MEDA transferable models achieved the average classification accuracy of 97.78% in source soil regions and 79.52% in target soil regions. The average accuracy of TCA based models and conventional SVM models were equivalent to each other and lower than MEDA models. Besides, the average running time of MEDA method (0.70 s) was much lower than TCA based method (21.90 s) and conventional SVM models (41.38 s). Overall, the results indicated that transfer learning approaches especially MEDA method could work in a more efficient manner than that of conventional multivariate analysis. The ultra-portable NIR sensor in combination with MEDA transfer learning algorithm as modelling method was a promising solution for low-cost and efficient field detection of plastic contaminated level in soil.

Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application.

Single-walled carbon nanotubes are of interest in biomedicine for imaging and molecular sensing applications and as shuttles for various cargos such as chemotherapeutic drugs, peptides, proteins, and oligonucleotides. Carbon nanotube surface chemistry can be modulated for subcellular targeting while preserving photoluminescence for label-free visualization in complex biological environments, making them attractive materials for such studies. The cell nucleus is a potential target for many pathologies including cancer and infectious diseases. Understanding mechanisms of nanomaterial delivery to the nucleus may facilitate diagnostics, drug development, and gene-editing tools. Currently, there are no systematic studies to understand how these nanomaterials gain access to the nucleus. Herein, we developed a carbon nanotube based hybrid material that elucidate a distinct mechanism of nuclear translocation of a nanomaterial in cultured cells. We developed a nuclear-targeted probe via cloaking photoluminescent single-walled carbon nanotubes in a guanidinium-functionalized helical polycarbodiimide. We found that the nuclear entry of the nanotubes was mediated by the import receptor importin β without the aid of importin α and not by the more common importin α/β pathway. Additionally, the nanotube photoluminescence exhibited distinct red-shifting upon entry to the nucleus, potentially functioning as a reporter of the importin β-mediated nuclear transport process. This work delineates a noncanonical mechanism for nanomaterial delivery to the nucleus and provides a reporter for the study of nucleus-related pathologies.