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Abstract Narrow-band imaging (NBI) is more efficient in detecting early gastrointestinal cancer than white light imaging (WLI). NBI technology is available only in conventional endoscopy, but unavailable in magnetic-assisted capsule endoscopy (MACE) systems due to MACE’s small size and obstacles in image processing issues. MACE is an easy, safe, and convenient tool for both patients and physicians to avoid the disadvantages of conventional endoscopy. Enabling NBI technology in MACE is mandatory. We developed a novel method to improve mucosal visualization using hyperspectral imaging (HSI) known as Spectrum Aided Visual Enhancer (SAVE, Transfer N, Hitspectra Intelligent Technology Co., Kaohsiung, Taiwan). The technique was developed by converting the WLI image captured by MACE to enhance SAVE images. The structural similarity index metric (SSIM) between the WLI MACE images and the enhanced SAVE images was 91%, while the entropy difference between the WLI MACE images and the enhanced SAVE images was only 0.47%. SAVE algorithm can identify the mucosal break on the esophagogastric junction in patients with gastroesophageal reflux disorder. We successfully developed a novel image-enhancing technique, SAVE, in the MACE system, showing close similarity to the NBI from the conventional endoscopy system. The future application of this novel technology in the MACE system can be promising.

Background/Objectives: Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection and accurate diagnosis. This review examines recent advancements in hyperspectral imaging (HSI), integrated with computer-aided diagnostic (CAD) techniques, to enhance HNC detection and diagnosis. Methods: A systematic review of seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and linear discriminant analysis (LDA). These are applicable to the hyperspectral imaging of HNC tissues. Results: The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. CNNs exhibit moderate performance, with an accuracy of 82%, sensitivity of 77%, and specificity of 86%. SVMs demonstrate the lowest performance, with an accuracy of 76% and sensitivity of 48%, but maintain a high specificity level at 89%. Additionally, in vivo studies demonstrate superior performance when compared to ex vivo studies, reporting higher accuracy (81%), sensitivity (83%), and specificity (79%). Conclusion: Despite these promising findings, challenges persist, such as HSI’s sensitivity to external conditions, the need for high-resolution and high-speed imaging, and the lack of comprehensive spectral databases. Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI’s clinical utility in HNC diagnostics. This review underscores the transformative potential of HSI and CAD techniques in revolutionizing HNC diagnostics, facilitating more accurate and earlier detection, and improving patient outcomes.

Abstract The clinical signs and symptoms of esophageal cancer (EC) are often not discernible until the intermediate or advanced phases. The detection of EC in advanced stages significantly decreases the survival rate to below 20%. This study conducts a comparative analysis of the efficacy of several imaging techniques, including white light image (WLI), narrowband imaging (NBI), cycle-consistent adversarial network simulated narrowband image (CNBI), and hyperspectral imaging simulated narrowband image (HNBI), in the early detection of esophageal cancer (EC). In conjunction with Kaohsiung Armed Forces General Hospital, a dataset consisting of 1000 EC pictures was used, including 500 images captured using WLI and 500 images captured using NBI. The CycleGAN model was used to generate the CNBI dataset. Additionally, a novel method for HSI imaging was created with the objective of generating HNBI pictures. The evaluation of the efficacy of these four picture types in early detection of EC was conducted using three indicators: CIEDE2000, entropy, and the structural similarity index measure (SSIM). Results of the CIEDE2000, entropy, and SSIM analyses suggest that using CycleGAN to generate CNBI images and HSI model for creating HNBI images is superior in detecting early esophageal cancer compared to the use of conventional WLI and NBI techniques.

Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks’ funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability.

This study introduces a novel method for detecting and measuring transparent glass sheets using hyperspectral imaging (HSI). The main goal of this study is to create a conversion technique that can accurately display spectral information from collected images, particularly in the visible light spectrum (VIS) and near-infrared (NIR) areas. This technique enables the capture of relevant spectral data when used with images provided by industrial cameras. The next step in this investigation is using principal component analysis to examine the obtained hyperspectral images derived from different treated glass samples. This analytical procedure standardizes the magnitude of light wavelengths that are inherent in the HSI images. The simulated spectral profiles are obtained using the generalized inverse matrix technique on the normalized HSI images. These profiles are then matched with spectroscopic data obtained from microscopic imaging, resulting in the observation of distinct dispersion patterns. The novel use of images coloring methods effectively displays the thickness of the glass processing sheet in a visually noticeable way. Based on empirical research, changes in the thickness of the glass coating in the NIR-HSI range cause significant changes in the transmission of infrared light at different wavelengths within the NIR spectrum. This phenomenon serves as the foundation for the study of film thickness. The root mean square error inside the NIR area is impressively low, calculated to be just 0.02. This highlights the high level of accuracy achieved by the technique stated above. Potential areas of investigation that arise from this study are incorporating the proposed approach into the design of a real-time, wide-scale automated optical inspection system.

Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot hyperspectral conversion, capable of converting RGB images into hyperspectral images (HSI). The integration of band selection with HSI has facilitated the identification of a set of narrow band images (NBI) from the RGB images. This study utilizes various iterations of the You Only Look Once (YOLO) machine learning (ML) framework to assess the precision, recall, and mean average precision in the detection of skin cancer. YOLO is commonly preferred in medical diagnostics due to its real-time processing speed and accuracy, which are essential for delivering effective and efficient patient care. The precision, recall, and mean average precision (mAP) of the SAVE images show a notable enhancement in comparison to the RGB images. This work has the potential to greatly enhance the efficiency of skin cancer detection, as well as improve early detection rates and diagnostic accuracy. Consequently, it may lead to a reduction in both morbidity and mortality rates.

Conventional diagnostic methods for glaucoma primarily rely on non-dynamic fundus images and often analyze features such as the optic cup-to-disc ratio and abnormalities in specific retinal locations like the macula and fovea. However, hyperspectral imaging techniques focus on detecting alterations in oxygen saturation within retinal vessels, offering a potentially more comprehensive approach to diagnosis. This study explores the diagnostic potential of hyperspectral imaging for glaucoma by introducing a novel hyperspectral imaging conversion technique. Digital fundus images are transformed into hyperspectral representations, allowing for a detailed analysis of spectral variations. Spectral regions exhibiting differences are identified through spectral analysis, and images are reconstructed from these specific regions. The Vision Transformer (ViT) algorithm is then employed for classification and comparison across selected spectral bands. Fundus images are used to identify differences in lesions, utilizing a dataset of 1291 images. This study evaluates the classification performance of models using various spectral bands, revealing that the 610–780 nm band outperforms others with an accuracy, precision, recall, F1-score, and AUC-ROC all approximately at 0.9007, indicating its superior effectiveness for the task. The RGB model also shows strong performance, while other bands exhibit lower recall and overall metrics. This research highlights the disparities between machine learning algorithms and traditional clinical approaches in fundus image analysis. The findings suggest that hyperspectral imaging, coupled with advanced computational techniques such as the ViT algorithm, could significantly enhance glaucoma diagnosis. This understanding offers insights into the potential transformation of glaucoma diagnostics through the integration of hyperspectral imaging and innovative computational methodologies.

Herein, we proposed a unique structural design for indium gallium nitride (InGaN) based blue resonant cavity micro-light-emitting diodes (RC-μ-LEDs), focusing on the design, fabrication, and the relevant performance analyses. The proposed RC-μ-LEDs possess a three-layer staggered InGaN/GaN multiple quantum wells (MQWs) within the nanoporous Distributed Bragg Reflectors (NP-DBRs) and the conventional DBRs, introducing light confinement within such a resonant cavity. A passivation layer using atomic layer deposition (ALD) is adopted to reduce the leakage current from sidewall defects as well. Consequently, for the resulting RC-μ-LEDs, the divergence angle (DA) can be achieved down to 39.04°. While the input current increases from 1.77 A/cm² to 54 A/cm², the peak wavelength will shift from 456.16 nm to 449.18 nm, a blue shift of only 6.98 nm. Finally, we also discuss the temperature-dependent characteristics and the corresponding behaviors of our RC-μ-LEDs. Our demonstrated RC-μ-LEDs exhibit great wavelength stability with a diminished divergence angle, thus enabling full-color and low-crosstalk micro-LED displays for on-demand high-resolution applications.

Abstract In this study, we present the growth of monolayer MoS2 (molybdenum disulfide) film. Mo (molybdenum) film was formed on a sapphire substrate through e-beam evaporation, and triangular MoS2 film was grown by direct sulfurization. First, the growth of MoS2 was observed under an optical microscope. The number of MoS2 layers was analyzed by Raman spectrum, atomic force microscope (AFM), and photoluminescence spectroscopy (PL) measurement. Different sapphire substrate regions have different growth conditions of MoS2. The growth of MoS2 is optimized by controlling the amount and location of precursors, adjusting the appropriate growing temperature and time, and establishing proper ventilation. Experimental results show the successful growth of a large-area single-layer MoS2 on a sapphire substrate through direct sulfurization under a suitable environment. The thickness of the MoS2 film determined by AFM measurement is about 0.73 nm. The peak difference between the Raman measurement shift of 386 and 405 cm−1 is 19.1 cm−1, and the peak of PL measurement is about 677 nm, which is converted into energy of 1.83 eV, which is the size of the direct energy gap of the MoS2 thin film. The results verify the distribution of the number of grown layers. Based on the observation of the optical microscope (OM) images, MoS2 continuously grows from a single layer of discretely distributed triangular single-crystal grains into a single-layer large-area MoS2 film. This work provides a reference for growing MoS2 in a large area. We expect to apply this structure to various heterojunctions, sensors, solar cells, and thin-film transistors.

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model’s performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5’s design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2–87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1–54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6–83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0–51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

Abstract One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In this study, a portable and low-cost snapshot hyperspectral imaging (HSI) algorithm-based housing module for differentiating between original and duplicate holograms was proposed. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system with a dimmer. A visible HSI algorithm that could convert an RGB image captured by the Raspberry Pi camera into a hyperspectral image was established. A specific region of interest was selected from the spectral image and mean gray value (MGV) and reflectivity were measured. Results suggested that shorter wavelengths are the most suitable for differentiating holograms when using MGV as the parameter for classification, while longer wavelengths are the most suitable when using reflectivity. The key features of this design include low cost, simplicity, lack of moving parts, and no requirement for an additional decoding key.

Mitochondria are critical for providing energy to maintain cell viability. Oxidative phosphorylation involves the transfer of electrons from energy substrates to oxygen to produce adenosine triphosphate. Mitochondria also regulate cell proliferation, metastasis, and deterioration. The flow of electrons in the mitochondrial respiratory chain generates reactive oxygen species (ROS), which are harmful to cells at high levels. Oxidative stress caused by ROS accumulation has been associated with an increased risk of cancer, and cardiovascular and liver diseases. Glutathione (GSH) is an abundant cellular antioxidant that is primarily synthesized in the cytoplasm and delivered to the mitochondria. Mitochondrial glutathione (mGSH) metabolizes hydrogen peroxide within the mitochondria. A long-term imbalance in the ratio of mitochondrial ROS to mGSH can cause cell dysfunction, apoptosis, necroptosis, and ferroptosis, which may lead to disease. This study aimed to review the physiological functions, anabolism, variations in organ tissue accumulation, and delivery of GSH to the mitochondria and the relationships between mGSH levels, the GSH/GSH disulfide (GSSG) ratio, programmed cell death, and ferroptosis. We also discuss diseases caused by mGSH deficiency and related therapeutics.

We propose a highly polarized vertical-cavity surface-emitting laser (VCSEL) consisting of staggered InGaN multiple quantum wells (MQWs), with the resonance cavity and polarization enabled by a bottom nanoporous (NP) n-GaN distributed Bragg reflectors (DBRs), and top TiO2 high-index contrast gratings (HCGs). Optoelectronic simulations of the 612 nm VCSEL were systematically and numerically investigated. First, we investigated the influences of the NP DBR and HCG geometries on the optical reflectivity. Our results indicate that when there are more than 17 pairs of NP GaN DBRs with 60% air voids, the reflectance can be higher than 99.7%. Furthermore, the zeroth-order reflectivity decreases rapidly when the HCG’s period exceeds 518 nm. The optimal ratios of width-to-period (52.86 ± 1.5%) and height-to-period (35.35 ± 0.14%) were identified. The staggered MQW design also resulted in a relatively small blue shift of 5.44 nm in the emission wavelength under a high driving current. Lastly, we investigated the cavity mode wavelength and optical threshold gain of the VCSEL with a finite size of HCG. A large threshold gain difference of approximately 67.4–74% between the 0th and 1st order transverse modes can be obtained. The simulation results in this work provide a guideline for designing red VCSELs with high brightness and efficiency.

Hydroxychloroquine, also known as quinine, is primarily utilized to manage various autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, and Sjogren’s syndrome. However, this drug has side effects, including diarrhea, blurred vision, headache, skin itching, poor appetite, and gastrointestinal discomfort. Blurred vision is caused by irreversible retinal damages and can only be mitigated by reducing hydroxychloroquine dosage or discontinuing the drug under a physician’s supervision. In this study, color fundus images were utilized to identify differences in lesions caused by hydroxychloroquine. A total of 176 color fundus images were captured from a cohort of 91 participants, comprising 25 patients diagnosed with hydroxychloroquine retinopathy and 66 individuals without any retinopathy. The mean age of the participants was 75.67 ± 7.76. Following the selection of a specific region of interest within each image, hyperspectral conversion technology was employed to obtain the spectrum of the sampled image. Spectral analysis was then conducted to discern differences between normal and hydroxychloroquine-induced lesions that are imperceptible to the human eye on the color fundus images. We implemented a deep learning model to detect lesions, leveraging four artificial neural networks (ResNet50, Inception_v3, GoogLeNet, and EfficientNet). The overall accuracy of ResNet50 reached 93% for the original images (ORIs) and 96% for the hyperspectral images (HSIs). The overall accuracy of Inception_v3 was 87% for ORIs and 91% for HSI, and that of GoogLeNet was 88% for ORIs and 91% for HSIs. Finally, EfficientNet achieved an overall accuracy of 94% for ORIs and 97% for HSIs.

From the farthest reaches of the universe to our own galaxy, there are many different celestial bodies that, even though they are very different, each have their own way of being beautiful. Earth, the planet with the best location, has been home to people for as long as we can remember. Even though we cannot be more thankful for all that Earth has given us, the human population needs to grow so that Earth is not the only place where people can live. Mars, which is right next to Earth, is the answer to this problem. Mars is the closest planet and might be able to support human life because it is close to Earth and shares many things in common. This paper will talk about how the first settlement on Mars could be planned and consider a 1000-person colony and the best place to settle on Mars, and make suggestions for the settlement’s technical, architectural, social, and economic layout. By putting together assumptions, research, and estimates, the first settlement project proposed in this paper will suggest the best way to colonize, explore, and live on Mars, which is our sister planet.

Abstract Characteristics of atomic layer deposition (ALD)-grown ZnO thin films on sapphire substrates with and without three-pulsed ozone (O3) as oxidant precursor and post-deposition thermal annealing (TA) are investigated. Deposition temperature and thickness of ZnO epilayers are 180 °C and 85 nm, respectively. Post-deposition thermal annealing is conducted at 300 °C in the ambience of oxygen (O2) for 1 h. With strong oxidizing agent O3 and post-deposition TA in growing ZnO, intrinsic strain and stress are reduced to 0.49% and 2.22 GPa, respectively, with extremely low background electron concentration (9.4 × 1015 cm−3). This is originated from a lower density of thermally activated defects in the analyses of thermal quenching of the integrated intensity of photoluminescence (PL) spectra. TA further facilitates recrystallization forming more defect-free grains and then reduces strain and stress state causing a remarkable decrease of electron concentration and melioration of surface roughness.

In this study, a snapshot-based hyperspectral imaging (HSI) algorithm that converts RGB images to HSI images is designed using the Raspberry Pi environment. A Windows-based Python application is also developed to control the Raspberry Pi camera and processor. The mean gray values (MGVs) of two distinct regions of interest (ROIs) are selected from three samples of 100 NTD Taiwanese currency notes and compared with three samples of counterfeit 100 NTD notes. Results suggest that the currency notes can be easily differentiated on the basis of MGV values within shorter wavelengths, between 400 nm and 500 nm. However, the MGV values are similar in longer wavelengths. Moreover, if an ROI has a security feature, then the classification method is considerably more efficient. The key features of the module include portability, lower cost, a lack of moving parts, and no processing of images required.

The vast universe, from its unfathomable ends to our very own Milky Way galaxy, is comprised of numerous celestial bodies—disparate yet each having their uniqueness. Amongst these bodies exist only a handful that have an environment that can nurture and sustain life. The Homo sapiens species has inhabited the planet, which is positioned in a precise way—Earth. It is an irrefutable truth that the planet Earth has provided us with all necessities for survival—for the human race to flourish and prosper and make scientific and technological advancements. Humans have always had an innate ardor for exploration—and now, since they have explored every nook and corner of this planet, inhabiting it and utilizing its resources, the time has come to alleviate the burden we have placed upon Earth to be the sole life-sustaining planet. With limited resources in our grasp and an ever-proliferating population, it is the need of the hour that we take a leap and go beyond the planet for inhabitation—explore the other celestial objects in our galaxy. Then, however, there arises a confounding conundrum—where do we go? The answer is right next to our home—the Red Planet, Mars. Space scientists have confirmed that Mars has conditions to support life and is the closest candidate for human inhabitation. The planet has certain similarities to Earth and its proximity provides us with convenient contact. This paper will be dealing with the conceptual design for the first city-state on Mars. Aggregating assumptions, research, and estimations, this first settlement project shall propose the most optimal means to explore, inhabit and colonize our sister planet, Mars.

Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.

Abstract This paper proposes a new encapsulation structure for aluminum nitride-based deep UV light-emitting diodes (DUV-LEDs) and eutectic flip chips containing polydimethylsiloxane (PDMS) fluid doped with SiO2 nanoparticles (NPs) with a UV-transparent quartz hemispherical glass cover. Experimental results reveal that the proposed encapsulation structure has considerably higher light output power than the traditional one. The light extraction efficiency was increased by 66.49% when the forward current of the DUV-LED was 200 mA. Doping the PDMS fluid with SiO2 NPs resulted in higher light output power than that of undoped fluid. The maximum efficiency was achieved at a doping concentration of 0.2 wt%. The optical output power at 200 mA forward current of the encapsulation structure with NP doping of the fluid was 15% higher than that without NP doping. The optical output power of the proposed encapsulation structure was 81.49% higher than that of the traditional encapsulation structure. The enhanced light output power was due to light scattering caused by the SiO2 NPs and the increased average refractive index. The encapsulation temperature can be reduced by 4 °C at a driving current of 200 mA by using the proposed encapsulation structure.

Air pollution has emerged as a global problem in recent years. Particularly, particulate matter (PM2.5) with a diameter of less than 2.5 μm can move through the air and transfer dangerous compounds to the lungs through human breathing, thereby creating major health issues. This research proposes a large-scale, low-cost solution for detecting air pollution by combining hyperspectral imaging (HSI) technology and deep learning techniques. By modeling the visible-light HSI technology of the aerial camera, the image acquired by the drone camera is endowed with hyperspectral information. Two methods are used for the classification of the images. That is, 3D Convolutional Neural Network Auto Encoder and principal components analysis (PCA) are paired with VGG-16 (Visual Geometry Group) to find the optical properties of air pollution. The images are classified into good, moderate, and severe based on the concentration of PM2.5 particles in the images. The results suggest that the PCA + VGG-16 has the highest average classification accuracy of 85.93%.

In this study, a biochip was fabricated using a light-absorbing layer of a silicon solar element combined with serrated, interdigitated electrodes and used to identify four different types of cancer cells: CE81T esophageal cancer, OE21 esophageal cancer, A549 lung adenocarcinoma, and TSGH-8301 bladder cancer cells. A string of pearls was formed from dielectrophoretic aggregated cancer cells because of the serrated interdigitated electrodes. Thus, cancer cells were identified in different parts, and electron–hole pairs were separated by photo-excited carriers through the light-absorbing layer of the solar element. The concentration catalysis mechanism of GSH and GSSG was used to conduct photocurrent response and identification, which provides the fast, label-free measurement of cancer cells. The total time taken for this analysis was 13 min. Changes in the impedance value and photocurrent response of each cancer cell were linearly related to the number of cells, and the slope of the admittance value was used to distinguish the location of the cancerous lesion, the slope of the photocurrent response, and the severity of the cancerous lesion. The results show that the number of cancerous cells was directly proportional to the admittance value and the photocurrent response for all four different types of cancer cells. Additionally, different types of cancer cells could easily be differentiated using the slope value of the photocurrent response and the admittance value.

In this study, n-type MoS2 monolayer flakes are grown through chemical vapor deposition (CVD), and a p-type Cu2O thin film is grown via electrochemical deposition. The crystal structure of the grown MoS2 flakes is analyzed through transmission electron microscopy. The monolayer structure of the MoS2 flakes is verified with Raman spectroscopy, multiphoton excitation microscopy, atomic force microscopy, and photoluminescence (PL) measurements. After the preliminary processing of the grown MoS2 flakes, the sample is then transferred onto a Cu2O thin film to complete a p-n heterogeneous structure. Data are confirmed via scanning electron microscopy, SHG, and Raman mapping measurements. The luminous energy gap between the two materials is examined through PL measurements. Results reveal that the thickness of the single-layer MoS2 film is 0.7 nm. PL mapping shows a micro signal generated at the 627 nm wavelength, which belongs to the B2 excitons of MoS2 and tends to increase gradually when it approaches 670 nm. Finally, the biosensor is used to detect lung cancer cell types in hydroplegia significantly reducing the current busy procedures and longer waiting time for detection. The results suggest that the fabricated sensor is highly sensitive to the change in the photocurrent with the number of each cell, the linear regression of the three cell types is as high as 99%. By measuring the slope of the photocurrent, we can identify the type of cells and the number of cells.

We produced an anodic aluminum oxide (AAO) structure with periodic nanopores on the surface of flip-chip blue light-emitting diodes (FC-BLEDs). The nanopores had diameters ranging from 73 to 85 nm and were separated by distances ranging from approximately 10 to 15 nm. The light extraction efficiency enhancement of the FC-BLEDs subjected to different durations of the second pore-widening process was approximately 1.6–2.9%. The efficiency enhancement may be attributed to the following mechanism: periodic nanopores on the surface of FC-BLEDs reduce the critical angle of total reflection and effective energy transfer from a light emitter into a surface plasmon mode produced by AAO.

Molybdenum disulfide (MoS2) was grown on a laser-processed periodic-hole sapphire substrate through chemical vapor deposition. The main purpose was to investigate the mechanism of MoS2 growth in substrate with a periodic structure. By controlling the amount and position of the precursor, adjusting the growth temperature and time, and setting the flow rate of argon gas, MoS2 grew in the region of the periodic holes. A series of various growth layer analyses of MoS2 were then confirmed by Raman spectroscopy, photoluminescence spectroscopy, and atomic force microscopy. Finally, the growth mechanism was studied by transmission electron microscopy (TEM). The experimental results show that in the appropriate environment, MoS2 can be successfully grown on substrate with periodic holes, and the number of growth layers can be determined through measurements. By observing the growth mechanism, composition analysis, and selected area electron diffraction diagram by TEM, we comprehensively understand the growth phenomenon. The results of this research can serve as a reference for the large-scale periodic growth of MoS2. The production of periodic structures by laser drilling is advantageous, as it is relatively simpler than other methods.

In this study, an algorithm to identify the maneuvers of a satellite is developed by comparing the Keplerian elements acquired from the two-line elements (TLEs) and Keplerian elements propagated from simplified perturbation models. TLEs contain a specific set of orbital elements, whereas the simplified perturbation models are used to propagate the state vectors at a given time. By comparing the corresponding Keplerian elements derived from both methods, a satellite’s maneuver is identified. This article provides an outline of the working methodology and efficacy of the method. The function of this approach is evaluated in two case studies, i.e., TOPEX/Poseidon and Envisat, whose maneuver histories are available. The same method is implemented to identify the station-keeping maneuvers for TDRS-3, whose maneuver history is not available. Results derived from the analysis indicate that maneuvers with a magnitude of even as low as cm/s are detected when the detection parameters are calibrated properly.

Abstract The features of eight-period In0.2Ga0.8N/GaN quantum wells (QWs) with silicon (Si) doping in the first two to five quantum barriers (QBs) in the growth sequence of blue light-emitting diodes (LEDs) are explored. Epilayers of QWs’ structures are grown on 20 pairs of In0.02Ga0.98N/GaN superlattice acting as strain relief layers (SRLs) on patterned sapphire substrates (PSSs) by a low-pressure metal-organic chemical vapor deposition (LP-MOCVD) system. Temperature-dependent photoluminescence (PL) spectra, current versus voltage (I-V) curves, light output power versus injection current (L-I) curves, and images of high-resolution transmission electron microscopy (HRTEM) of epilayers are measured. The consequences show that QWs with four Si-doped QBs have larger carrier localization energy (41 meV), lower turn-on (3.27 V) and breakdown (− 6.77 V) voltages, and higher output power of light of blue LEDs at higher injection current than other samples. Low barrier height of QBs in a four-Si-doped QB sample results in soft confinement potential of QWs and lower turn-on and breakdown voltages of the diode. HRTEM images give the evidence that this sample has relatively diffusive interfaces of QWs. Uniform spread of carriers among eight QWs and superior localization of carriers in each well are responsible for the enhancement of light output power, in particular, for high injection current in the four-Si-doped QB sample. The results demonstrate that four QBs of eight In0.2Ga0.8N/GaN QWs with Si doping not only reduce the quantum-confined Stark effect (QCSE) but also improve the distribution and localization of carriers in QWs for better optical performance of blue LEDs.

In this work, textured, well-faceted ZnO materials grown on planar Si(100), planar Si(111), and textured Si(100) substrates by low-pressure chemical vapor deposition (LPCVD) were analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and cathode luminescence (CL) measurements. The results show that ZnO grown on planar Si(100), planar Si(111), and textured Si(100) substrates favor the growth of ZnO(110) ridge-like, ZnO(002) pyramid-like, and ZnO(101) pyramidal-tip structures, respectively. This could be attributed to the constraints of the lattice mismatch between the ZnO and Si unit cells. The average grain size of ZnO on the planar Si(100) substrate is slightly larger than that on the planar Si(111) substrate, while both of them are much larger than that on the textured Si(100) substrate. The average grain sizes (about 10–50 nm) of the ZnO grown on the different silicon substrates decreases with the increase of their strains. These results are shown to strongly correlate with the results from the SEM, AFM, and CL as well. The reflectance spectra of these three samples show that the antireflection function provided by theses samples mostly results from the nanometer-scaled texture of the ZnO films, while the micrometer-scaled texture of the Si substrate has a limited contribution. The results of this work provide important information for optimized growth of textured and well-faceted ZnO grown on wafer-based silicon solar cells and can be utilized for efficiency enhancement and optimization of device materials and structures, such as heterojunction with intrinsic thin layer (HIT) solar cells.

This paper proposes a method to detect air pollution by applying a hyperspectral imaging algorithm for visible light, near infrared, and far infrared. By assigning hyperspectral information to images from monocular, near infrared, and thermal imaging, principal component analysis is performed on hyperspectral images taken at different times to obtain the solar radiation intensity. The Beer–Lambert law and multivariate regression analysis are used to calculate the PM2.5 and PM10 concentrations during the period, which are compared with the corresponding PM2.5 and PM10 concentrations from the Taiwan Environmental Protection Agency to evaluate the accuracy of this method. This study reveals that the accuracy in the visible light band is higher than the near-infrared and far-infrared bands, and it is also the most convenient band for data acquisition. Therefore, in the future, mobile phone cameras will be able to analyze the PM2.5 and PM10 concentrations at any given time using this algorithm by capturing images to increase the convenience and immediacy of detection.

P-type and N-type photoelectrochemical (PEC) biosensors were established in the laboratory to discuss the correlation between characteristic substances and photoactive material properties through the photogenerated charge carrier transport mechanism. Four types of human esophageal cancer cells (ECCs) were analyzed without requiring additional bias voltage. Photoelectrical characteristics were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), UV–vis reflectance spectroscopy, and photocurrent response analyses. Results showed that smaller photocurrent was measured in cases with advanced cancer stages. Glutathione (L-glutathione reduced, GSH) and Glutathione disulfide (GSSG) in cancer cells carry out redox reactions during carrier separation, which changes the photocurrent. The sensor can identify ECC stages with a certain level of photoelectrochemical response. The detection error can be optimized by adjusting the number of cells, and the detection time of about 5 min allowed repeated measurement.

Increasing attention has been paid to two-dimensional (2D) materials because of their superior performance and wafer-level synthesis methods. However, the large-area characterization, precision, intelligent automation, and high-efficiency detection of nanostructures for 2D materials have not yet reached an industrial level. Therefore, we use big data analysis and deep learning methods to develop a set of visible-light hyperspectral imaging technologies successfully for the automatic identification of few-layers MoS2. For the classification algorithm, we propose deep neural network, one-dimensional (1D) convolutional neural network, and three-dimensional (3D) convolutional neural network (3D-CNN) models to explore the correlation between the accuracy of model recognition and the optical characteristics of few-layers MoS2. The experimental results show that the 3D-CNN has better generalization capability than other classification models, and this model is applicable to the feature input of the spatial and spectral domains. Such a difference consists in previous versions of the present study without specific substrate, and images of different dynamic ranges on a section of the sample may be administered via the automatic shutter aperture. Therefore, adjusting the imaging quality under the same color contrast conditions is unnecessary, and the process of the conventional image is not used to achieve the maximum field of view recognition range of ~1.92 mm2. The image resolution can reach ~100 nm and the detection time is 3 min per one image.

A highly sensitive photoelectrochemical (PEC) biosensor without external bias was developed in this study. The biosensor was configured with a p-Cu2O and n-ZnO heterostructure. Hexamethylenetetramine (HMTA) and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) was used to improve the crystal structure of Cu2O and ZnO and reduce the defects in the Cu2O/ZnO interface. This fabrication method provided the highly crystallized Cu2O/ZnO structure with excellent electrical property and photoresponse in visible light. The structure was applied to a biosensor for detecting two different cancerous levels of esophageal cells, namely, OE21 and OE21-1, with a high gain in photocurrent (5.8 and 6.2 times, respectively) and a low detection limit (3000 cells in 50 μL). We believe that such a p-n heterojunction PEC biosensor could advance biosensor development and provide a promising candidate for biomedical applications.

Light-emitting diodes (LEDs) have numerous advantages. However, LEDs only offer a point light source. Therefore, transforming LEDs into planar light sources is a new objective in general lighting applications. Solid light guides have strong uniformity but are marred by their material absorption characteristics and weight. Hollow light guides constitute a solution to the weight problem but exhibit poor uniformity and necessitate sacrificing efficiency to enhance uniformity. To resolve the uniformity, weight, and efficiency problems simultaneously, we propose a hollow light guide architecture involving mini-LEDs with asymmetric luminous intensity. To develop this guide module, we first optimized the aspect ratio of the cavity and then modulated the light path by using varied angles of the reflection surface on the end wall of the module. We then designed a beveled reflection surface near the mini-LEDs to further enhance uniformity. An archetype of the proposed architecture for planar light source modules had a width and depth of 51.5 and 9.95 mm, respectively. Experimental results revealed a total efficiency of 83.9% and uniformity of 92.3%. The module weight was determined to be 215 g, which was 40% lighter than that of similarly sized solid light guide modules.

InGaN/GaN samples grown on c-plane sapphire substrate with different In concentrations by metal organic chemical vapor deposition are demonstrated. The subsequent capping GaN layer growth opens a possibility for dislocation reduction due to the lateral strain relaxation in growth geometry. We present the further growth optimization and innovative characterization of InGaN layers overgrown on different structures with varying In concentrations. The photoelectrical and optical properties of the InGaN layers with/without capping GaN layer are investigated by time-resolved picosecond transient grating and temperature dependence photoluminescence. We note a 10-fold increase in carrier lifetime in the InGaN layers when the sample structure changed from PIN to single InGaN layer.

This study developed an optical module for laser headlamps with a tunable light field. Laser headlamps have high and low beam lighting functions that are applied by changing the numbers of unit headlamps. For regulatory validation, we validated the project in accordance with the Regulation No. 112 of the Economic Commission for Europe (ECE) to determine whether the results meet the regulatory requirements or not. The proposed design increased the lighting distance by 2.14 times and decreased the size of the mechanism by 45% relative to the values for LED headlamps. High and low beam lighting functions were provided by the same laser sources.