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Although smoke detectors are actively being studied to reduce false fire alarms, they still face challenging issues such as complex and elaborate alignment, high cost, large size, and poor performance. In particular, most smoke detection systems based on Mie scattering, which rely on single-scattering measurements, may not perform effectively in real-world environments where multiple scattering occurs. We present an advanced smoke detection instrument for aspirating smoke detection and classification based on multiple scattering. Multi-angle light scattering with an LED array instead of angle-positioned PDs was measured, and the unique optical property ratios of fire and non-fire aerosols were calculated. The feasibility of smoke detection and classification was verified by evaluating the classification performance of 10 types of fire and non-fire aerosols using general supervised learning algorithms. The advanced smoke detection instrument features a simple design, making it cost-effective and compact. In addition to reducing false fire alarms, it is expected to contribute to choosing appropriate fire extinguishers based on fire class and advancing research of complex fire detection.

Conventional methods for analyzing heavy metal contamination in soil and water generally require laboratory equipped instruments, complex procedures, skilled personnel and a significant amount of time. With the advancement in computing and multitasking performances, smartphone-based sensors potentially allow the transition of the laboratory-based analytical processes to field applicable, simple methods. In the present work, we demonstrate the novel miniaturized setup for simultaneous sample preparation and smartphone-based optical sensing of arsenic As(III) in the contaminated soil. Colorimetric detection protocol utilizing aptamers, gold nanoparticles and NaCl have been optimized and tested on the PDMS-chip to obtain the high sensitivity with the limit of detection of 0.71 ppm (in the sample) and a correlation coefficient of 0.98. The performance of the device is further demonstrated through the comparative analysis of arsenic-spiked soil samples with standard laboratory method, and a good agreement with a correlation coefficient of 0.9917 and the average difference of 0.37 ppm, are experimentally achieved. With the android application on the device to run the experiment, the whole process from sample preparation to detection is completed within 3 hours without the necessity of skilled personnel. The approximate cost of setup is estimated around 1 USD, weight 55 g. Therefore, the presented method offers the simple, rapid, portable and cost-effective means for onsite sensing of arsenic in soil. Combined with the geometric information inside the smartphones, the system will allow the monitoring of the contamination status of soils in a nation-wide manner.

We present a portable multi-contrast microscope capable of producing bright-field, dark-field, and differential phase contrast images of thin biological specimens on a smartphone platform. The microscopy method is based on an imaging scheme termed “color-coded light-emitting-diode (LED) microscopy (cLEDscope),” in which a specimen is illuminated with a color-coded LED array and light transmitted through the specimen is recorded by a color image sensor. Decomposition of the image into red, green, and blue colors and subsequent computation enable multi-contrast imaging in a single shot. In order to transform a smartphone into a multi-contrast imaging device, we developed an add-on module composed of a patterned color micro-LED array, specimen stage, and miniature objective. Simple installation of this module onto a smartphone enables multi-contrast imaging of transparent specimens. In addition, an Android-based app was implemented to acquire an image, perform the associated computation, and display the multi-contrast images in real time. Herein, the details of our smartphone module and experimental demonstrations with various biological specimens are presented.

This paper proposes a simple, high-efficiency refractive index (RI) sensor, with a structure based on the planar lightwave circuit (PLC) probe type. The optical sensor has a 1 × 2 splitter structure with reference and sensing channels, each consisting of a U-shaped waveguide structure that is configured by connecting C bends. This design allows for the sensor device to have a probe structure wherein the surface interconnected with activity devices (i.e., an optical source and optical detector) is placed on one side. The reference channel is bent with a minimum optical loss, and the sensing channel has a bent structure, involving a C-bend waveguide with a maximum loss. The C-bend waveguide with a maximum loss is conformally aligned to have a trench structure with the same bending radius, designed to selectively expose the sidewall of the core layer. The local index contrast varies depending on the material in contact with the trench, resulting in a change in the optical output power of the waveguide. The sensitivity of the proposed sensor was 0 and 2070 μW/refractive index unit (RIU) for the reference and sensing channels, respectively, as the RI changed from 1.385 to 1.445 at a 1550 nm wavelength. These results suggest that the proposed structure enables efficient RI measurement through the use of a simple dip-type method.

Smoke detectors are the most widely used fire detectors due to their high sensitivity. However, they have persistently faced issues with false alarms, known as nuisance alarms, as they cannot distinguish smoke particles, and their responsiveness varies depending on the particle size and concentration. Although technologies for distinguishing smoke particles have shown promising results, the hardware limitations of smoke detectors necessitate an intelligent approach to analyze scattering signals of various wavelengths and their temporal changes. In this paper, we propose a pipeline that can distinguish smoke particles based on scattering signals of various wavelengths as input. In the data extraction phase, we propose methods for extracting datasets from time series data. We propose a method that combines traditional approaches, early detection methods, and a Dynamic Time Warping technique that utilizes only the shape of the signal without preprocessing. In the learning model and classification phase, we present a method to select and compare various architectures and hyperparameters to create a model that achieves the best classification performance for time series data. We create datasets for six different targets in our presented sensor and smoke particle test environment and train classification models. Through performance comparisons, we identify architecture and parameter combinations that achieve up to 98.7% accuracy. Ablation studies under various conditions demonstrate the validity of the chosen architecture and the potential of other models.

We describe the development and clinical evaluation of an automated smartphone-linked sensor capable of chemical-free, quantitative measurement of hemoglobin concentration ([Hb]) in whole blood samples. We have demonstrated that our sensor could analyze an unprocessed blood specimen with a mean processing time of <8 s and provided the [Hb] results with ~99% accuracy against a reference hematology analyzer with coefficient of variation (CV) of 1.21% measured at [Hb] = 11.2 g/dL. Its diagnostic capability for anemia was evaluated by measuring [Hb] of 142 clinical blood specimens and comparing the results with those from an automated hematology analyzer (ADVIA 2120i, Siemens AG, Germany) and a portable hemoglobinomteter (Hb201+, Hemocue, Sweden). The sensor yielded comparable sensitivities and specificities of 87.50% and 100.00% for males, and 94.44% and 100.00% for females, respectively, for anemic detection. The results suggested that our optical sensor based on the intrinsic photothermal response of Hb molecules and advances in consumer electronics, particularly smartphone capabilities, enables a direct, chemical-free [Hb] assay accessible to people in both developed and developing countries.

Structural arrangement and dimension play vital roles in wave transport and amplification as they can restrict the volume explored by the waves. However, it is challenging to systematically investigate the interplay among structural, optical, and mechanical properties, in part because of limited experimental platforms that modulate the structural arrangement in a continuous manner. We present light amplification action in Rhodamine B doped silk fibroin (SF) nanofibrous scaffolds and its modulation via the control of the alignment or directionality of SF nanofibers through an electrospinning procedure. Random lasing features of such scaffolds are examined as a function of structural arrangement of the SF nanofibers, and optical-structural-mechanical relationships of the SF-based structures are examined. As SF nanofibers are aligned parallel undergoing a transition from three to quasi-two dimension, light amplification features (e.g., lasing threshold and output power) enhanced, which also strongly correlated with mechanical characteristics (i.e., Young’s moduli) of the scaffolds. We confirm such optical characteristics using quasi-mode analyses based on the finite element method. We further demonstrate non-contact, in situ measurement of alternations in lasing features of the scaffolds while the specimens are under tensile loads. These results may highlight potential utility of the scaffolds as a flexible and biocompatible sensor.

We present our aeromagnetic survey results from an investigation of the iron ore mineral distribution in Pocheon, Korea, in the west-central area of the Korean Peninsula. A manned aeromagnetic system using a helicopter for regional exploration and an unmanned aeromagnetic system using a multicopter for high-resolution exploration were used for the survey. The inversion results of the magnetic data confirmed the possibility of the existence of a new iron ore body. Drilling was carried out based on inversion results and drilling revealed amphibolite including iron ore, as indicated by a strong magnetic response. The position and depth of the iron ore were consistent with the interpretation results of the magnetic data.