An exhaled breath gas sensor system for near-infrared ammonia measurement and kidney diagnostics.

Breath analysis enables rapid, noninvasive diagnosis of human health by identifying and quantifying exhaled biomarker. Here, we demonstrated an exhaled breath sensing method using the near-infrared laser spectroscopy, and sub parts-per-million (ppm) level ammonia detection inside the exhaled gas was achieved employing a distributed feedback laser centered at 1512 nm and Kalman filtering algorithm. Integration of the ammonia sensor was realized for exhaled breath analysis of kidney patients, and a dual operation mechanism with static and dynamic modes was proposed to make this method applicable for real-time and comprehensive pre-diagnosis of kidney disease.

Microelectronic printed chitosan/chondroitin sulfate/ZnO flexible and environmentally friendly triboelectric nanogenerator.

The triboelectric nanogenerator (TENG) of natural biomaterials is a new type of energy harvesting device and can be used as a self-powered sensor, which has received extensive research and attention. In this paper, based on the biocompatibility of chitosan and chondroitin sulfate, ZnO-modified chitosan/chondroitin sulfate/ZnO TENG was prepared for research on wearable devices and sustainable power supply devices. This study employs molecular dynamics to compute the interaction energy between chitosan and ZnO molecules. Theoretical calculations have unequivocally substantiated the occurrence of a binding interaction between these two molecular entities. The effect of ZnO on chitosan/chondroitin sulfate morphology was investigated by atomic force microscopy. The chitosan/chondroitin sulfate/ZnO TENG has high flexibility and electrical output performance. It can reach 105 V and 3.3 µA of open-circuit voltage and short-circuit current. Chitosan/chondroitin Sulfate/ZnO TENG successfully converts the mechanical energy of human motion into electrical energy. Strong electrical signals are exhibited when making fists and waving fingers and wrists. The TENG is a self-powered source and lights up 70 blue light-emitting diodes (LEDs). The chitosan/chondroitin sulfate/ZnO TENG has demonstrated its capabilities in energy harvesting and wearable self-powered sensors.

Composition design and optimization of Fe-C-Mn-Al steel based on machine learning.

The purpose of this study is to explore the composition space of Fe-C-Mn-Al steel using machine learning in order to identify materials with high-strength mechanical properties. A dataset of 580 steel samples was collected from the literature, each containing information on elemental composition, heat treatment processes, specimen dimensions, and mechanical properties (ultimate tensile strength and total elongation). Eight common machine learning models were constructed to predict the ultimate tensile strength (UTS) and total elongation (TE) of the steel. It was observed that the random forest regression (RFR) model, when trained, demonstrated superior overall performance in predicting UTS, with an average absolute error of approximately 90 MPa, and TE, with an average absolute error of about 7.9%. Validation of the model using eight sets of data that were not part of the dataset revealed that the predictions were in close agreement with experimental results, indicating the strong predictive capability of the RFR model. Subsequently, the trained RFR model was used to explore the composition space of Fe-C-Mn-Al steel, identifying the top fifty combinations of elemental compositions and heat treatment parameters, all of which manifest high ultimate tensile strength (UTS). This provides valuable research directions and methods to expedite the development of high-strength Fe-C-Mn-Al steel.

Slow-Light-Enhanced Polarization Division Multiplexing Infrared Absorption Spectroscopy for On-Chip Wideband Multigas Detection in a 1D Photonic Crystal Waveguide.

Slow-light photonic crystal waveguide (PCW) gas sensors based on infrared absorption spectroscopy play a pivotal role in enhancing the on-chip interaction between light and gas molecules, thereby significantly boosting sensor sensitivity. However, two-dimensional (2D) PCWs are limited by their narrow mode bandwidth and susceptibility to polarization, which restricts their ability for multigas measurement. Due to quasi-TE and quasi-TM mode guiding characteristics in one-dimensional (1D) PCW, a novel slow-light-enhanced polarization division multiplexing infrared absorption spectroscopy was proposed for on-chip wideband multigas detection. The optimized 1D PCW gas sensor experimentally shows an impressive slow-light mode bandwidth exceeding 100 nm (TM, 1500-1550 nm; TE, 1610-1660 nm) with a group index ranging from 4 to 25 for the two polarizations. The achieved bandwidth in the 1D PCW is 2-3 times that of the reported quasi-TE polarized 2D PCWs. By targeting the absorption lines of different gas species, multigas detection can be realized by modulating the lasers and demodulating the absorption signals at different frequencies. As an example, we performed dual-gas measurements with the 1D PCW sensor operating in TE mode at 1.65 µm for methane (CH4) detection and in TM mode at 1.53 µm for acetylene (C2H2) detection. The 1 mm long sensor achieved a remarkable limit of detection (LoD) of 0.055% for CH4 with an averaging time of 17.6 s, while for C2H2, the LoD was 0.18%. This polarization multiplexing sensor shows great potential for on-chip gas measurement because of the slow-light enhancement in the light-gas interaction effect as well as the large slow-light bandwidth for multigas detection.

Near-Infrared Off-Axis Cavity-Enhanced Optical Frequency Comb Spectroscopy for CO2/CO Dual-Gas Detection Assisted by Machine Learning.

Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields. For the on-axis coupling incidence scheme, the detection accuracy and stability are seriously affected by the cavity-mode noise, and therefore, stable operation inevitably requires external electronic mode-locking and sweeping devices, substantially increasing system complexity. To address this issue, we propose off-axis cavity-enhanced optical frequency comb spectroscopy from both theoretical and experimental aspects, which is applied to the detection of single- and dual-gas of carbon monoxide (CO) and carbon dioxide (CO2) in the near-infrared. An erbium-doped fiber frequency comb with a repetition frequency of ∼41.709 MHz is coupled into a resonant cavity with a length of ∼360 mm in an off-axis manner, exciting numerous high-order modes to effectively suppress cavity-mode noise. The performance of multiple machine learning models is compared for the inversion of a single/dual gas concentration. A few absorbance spectra are collected to build a sample data set, which is then utilized for model training and learning. The results demonstrate that the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model achieves the highest predictive accuracy for gas concentration and is ultimately applied to the detection system. Based on Allan deviation, the detection limit for CO in single-gas detection can reach 8.247 parts per million by volume (ppmv) by averaging 87 spectra. Meanwhile, for simultaneous CO2/CO measurement with highly overlapping absorbance spectra, the LoD can be reduced to 13.196 and 4.658 ppmv, respectively. The proposed optical gas sensing technique indicates the potential for the development of a field-deployable and intelligent sensor system capable of simultaneous detection of multiple gases.

A potential therapeutic drug for osteoporosis: prospect for osteogenic LncRNAs.

Long non-coding RNAs (LncRNAs) play essential roles in multiple physiological processes including bone formation. Investigators have revealed that LncRNAs regulated bone formation through various signaling pathways and micro RNAs (miRNAs). However, several problems exist in current research studies on osteogenic LncRNAs, including sophisticated techniques, high cost for in vivo experiment, as well as low homology of LncRNAs between animal model and human, which hindered translational medicine research. Moreover, compared with gene editing, LncRNAs would only lead to inhibition of target genes rather than completely knocking them out. As the studies on osteogenic LncRNA gradually proceed, some of these problems have turned osteogenic LncRNA research studies into slump. This review described some new techniques and innovative ideas to address these problems. Although investigations on osteogenic LncRNAs still have obtacles to overcome, LncRNA will work as a promising therapeutic drug for osteoporosis in the near future.

Protocol for measuring visual preferences of freely swimming Xenopus laevis tadpoles.

Xenopus tadpoles display innate visually guided behaviors which are thought to promote survival by guiding them toward sources of food and away from predators. Experimentally, studying these behaviors can provide insight into the formation and function of the neural circuits which underlie them. Here, we present a protocol for measuring visual preferences of freely swimming tadpoles. We describe steps to create the visual stimuli, carry out the experiments, and analyze the resulting data. For complete details on the use and execution of this protocol, please refer to Hunt et al.1 and Bruno et al.2.

WMS-based near-infrared on-chip acetylene sensor using polymeric SU8 Archimedean spiral waveguide with Euler S-bend.

SU8 is a cost-effective polymer material that is highly suitable for large-scale fabrication of waveguides. However, it has not been employed for on-chip gas measurement utilizing infrared absorption spectroscopy. In this study, we propose a near-infrared on-chip acetylene (C2H2) sensor using SU8 polymer spiral waveguides for the first time to our knowledge. The performance of the sensor based on wavelength modulation spectroscopy (WMS) was experimentally validated. By incorporating the proposed Euler-S bend and Archimedean spiral SU8 waveguide, we achieved a reduction in the sensor's size by over fifty percent. Leveraging the WMS technique, we evaluated the C2H2 sensing performance at 1532.83 nm for SU8 waveguides of lengths 7.4 cm and 13 cm. The limit of detection (LoD) values were 2197.1 ppm (parts per million) and 425.5 ppm, respectively, with an averaging time of 0.2 s. Furthermore, the experimentally obtained optical power confinement factor (PCF) closely approximated the simulated value, with a value of 0.0172 compared to the simulated value of 0.016. The waveguide loss is measured to be 3 dB/cm. The rise time and fall time were approximately 2.05 s and 3.27 s, respectively. This study concludes that the SU8 waveguide exhibits significant potential for high-performance on-chip gas sensing in the near-infrared wavelength range.

An iron-base oxygen-evolution electrode for high-temperature electrolyzers.

High-temperature molten-salt electrolyzers play a central role in metals, materials and chemicals production for their merit of favorable kinetics. However, a low-cost, long-lasting, and efficient high-temperature oxygen evolution reaction (HT-OER) electrode remains a big challenge. Here we report an iron-base electrode with an in situ formed lithium ferrite scale that provides enhanced stability and catalytic activity in both high-temperature molten carbonate and chloride salts. The finding is stemmed from a discovery of the ionic potential-stability relationship and a basicity modulation principle of oxide films in molten salt. Using the iron-base electrode, we build a kiloampere-scale molten carbonate electrolyzer to efficiently convert CO2 to carbon and oxygen. More broadly, the design principles lay the foundations for exploring cheap, Earth-abundant, and long-lasting HT-OER electrodes for electrochemical devices with molten carbonate and chloride electrolytes.

Small changes in thermal conditions hinder marathon running performance in the tropics.

We examined marathon performance of the same group of runners in relation to small changes in dry bulb temperature (Tdb) and wet bulb temperature (Twb) across 3 consecutive y, and investigated whether performance was poorer during an evening marathon compared with morning marathons. Marathon results were obtained from the 2017, 2018, and 2019 Standard Chartered Singapore Marathons. Tdb, Twb, Td, relative humidity, and absolute humidity were gathered for each marathon. K-means clustering and linear regressions were performed on 610 runners who participated in all three marathons. Analysis of the 610 runners' marathon performance was contrasted with Tdb and Twb. Linear regressions were also performed on 190 runners filtered by percentile, yielding similar results. For clusters with similar Tdb from all runners K-means clustering, an increase in mean Twb by 1.5°C coincided with an increase in finishing time by 559 s (9.3 min) (p < 0.033). Twb hinders marathon performance more than Tdb, with each percentage rise in Tdb and Twb resulting in an increase in net time by 7.6% and 39.1%, respectively (p < 0.025). Male and female runners' response to Tdb and Twb changes were similar (overlap in 95% confidence intervals for the respective regression coefficients). In conclusion, small variations in environmental parameters affected marathon performance, with Twb impairing marathon performance more than Tdb. Marathon performance was likely better in the morning than evening, possibly due to time of day differences, along with unfavorable Tdb that superseded training effects and the effects of lower Twb.

Study on the Influence of Graphene Content Variation on the Microstructure Evolution and Properties of Laser Additive Manufacturing Nickel-Based/SiC Composite Cladding Layer on Aluminum Alloy Surface.

A Ni25-10% SiC-X% graphene (mass fraction X = 0, 0.5, 1.0, 1.5) composite cladding layer was prepared on a 6063 aluminum alloy substrate using laser cladding in order to enhance the comprehensive performance of the aircraft refueling interface. The effect of the graphene content on the organization and properties of nickel-based silicon carbide composite cladding layers was investigated by laser melting. The macroscopic morphology, microstructure, hardness, elemental changes, corrosion and wear resistance of the cladding layer were studied by optical microscopy, scanning electron microscopy, a hardness tester, an X-ray diffractometer, an electrochemical workstation and an M-2000 frictional wear tester. The results indicated that the nickel-based clad layer without graphene incorporation had the worst forming, with a large number of pores and cracks in the cladding layer. Because graphene agglomerated easily, cracks were regenerated when the content of graphene was higher than 0.5%. The material phases of the cladding layer without graphene incorporation were mainly: Al3Ni2, Fe3Si and SiC. Due to the addition of graphene, the clad layer of specimen 2 was refined and a large number of hard phases, such as CrC and Cr23C6, were generated, which led to the increase in the hardness of the clad layer. When the content of graphene was further increased, the number of hard phases such as CrC and Cr23C6 produced in the cladding was relatively reduced due to the agglomeration of graphene, and the hardness of the cladding was reduced. As the impermeability of graphene reduces the diffusion of corrosive media to the substrate, the generation of hard-phase Al3Ni2 in the cladding layer makes the corrosion resistance of the cladding layer increase with the increase in graphene mass fraction. The result is that, when the content of graphene was 0.5%, the overall performance of the clad layer was the best, where its average hardness was increased by 40%, the average coefficient of friction was reduced by 12.7% and the wear rate was reduced by 60%.

Development of a mid-infrared sensor system for early fire identification in cotton harvesting operations.

To realize early fire identification in cotton harvesting operations, a mid-infrared carbon monoxide (CO) sensor system was developed. To match the broadband light source with a 15° divergence angle, a multipass gas cell (MPGC) with an effective path length of 180 cm was designed to improve sensor sensitivity, leading to a limit of detection (LoD) of 0.83 parts-per-million by volume (ppmv). A damping module with springs at the bottom and front/back sides was fabricated, which can effectively reduce the vibration intensity by >80%. The sensor system can operate normally from -40 °C to 85 °C by stabilizing the temperature of the optical module through heating or cooling as well as using automotive electronic components. An adaptive early fire identification algorithm based on a dual-parameter threshold alarming method was proposed to avoid false and missing alarms. Field deployments on a harvester verified the good practicability of the sensor system.

A Fast Adaptive Multi-Scale Kernel Correlation Filter Tracker for Rigid Object.

The efficient and accurate tracking of a target in complex scenes has always been one of the challenges to tackle. At present, the most effective tracking algorithms are basically neural network models based on deep learning. Although such algorithms have high tracking accuracy, the huge number of parameters and computations in the network models makes it difficult for such algorithms to meet the real-time requirements under limited hardware conditions, such as embedded platforms with small size, low power consumption and limited computing power. Tracking algorithms based on a kernel correlation filter are well-known and widely applied because of their high performance and speed, but when the target is in a complex background, it still can not adapt to the target scale change and occlusion, which will lead to template drift. In this paper, a fast multi-scale kernel correlation filter tracker based on adaptive template updating is proposed for common rigid targets. We introduce a simple scale pyramid on the basis of Kernel Correlation Filtering (KCF), which can adapt to the change in target size while ensuring the speed of operation. We propose an adaptive template updater based on the Mean of Cumulative Maximum Response Values (MCMRV) to alleviate the problem of template drift effectively when occlusion occurs. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods based on a kernel correlation filter.

Vehicle-Deployed Off-Axis Integrated Cavity Output Spectroscopic CH4/C2H6 Sensor System for Mobile Inspection of Natural Gas Leakage.

A vehicle-deployed parts-per-billion in volume (ppbv)-level off-axis integrated cavity output spectroscopic (OA-ICOS) CH4/C2H6 sensor system was experimentally presented for mobile inspection of natural gas leakage in urban areas. For the time-division-multiplexing-based dual-gas sensor system, an antivibration 35-cm-long optical cavity with an effective path length of ∼2510 m was fabricated with a high-stability temperature and pressure control design. An Allan deviation analysis yielded a minimum detection limit of 0.2 ppbv for CH4 detection and 10 ppbv for C2H6 detection for a 1 s averaging time. A natural gas leakage source location algorithm was proposed using an improved hybrid Nelder-Mead simplex search method and a particle swarm optimization (NM-PSO) algorithm. For field industrial application, the accuracy of the sensor system and leakage source location algorithm was confirmed through a CH4/C2H6 cylinder leakage experiment on the campus. Furthermore, through natural gas pipeline network inspection measurements in urban areas, three types of leakage sources, including natural gas, biogas, and possible leakage source were respectively located and confirmed using the global positioning system and wind speed and direction measurement system, verifying the reliability and potential application of the vehicle-deployed inspection system for future natural gas pipeline leakage monitoring.

Estimating the bioavailability of acetochlor to wheat using in situ pore water and passive sampling.

The intensive use of acetochlor in China leads to its extensive existence in soil which may result in contamination of crops and commodities. Therefore, it is vital to assess the bioavailability and phytotoxicity of acetochlor to crops. In this study, four measurements involved in in situ pore water extraction (CIPW), passive sampling extraction (Cfree), ex situ pore water extraction (CEPW), and organic solvent extraction (Csoil) were conducted to assess the bioavailability and phytotoxicity of acetochlor to wheat plant plants in five soils. The results showed that the acetochlor concentrations accumulated in wheat foliage and roots were in the range of 0.11-0.87 mg/kg and 0.09-2.02 mg/kg in the five tested soils, respectively, and had a significant correlation with the acetochlor values analyzed by CIPW (R2 = 0.83-0.90, p < 0.0001) or the Cfree method (R2 = 0.86-0.92, p < 0.0001). The acetochlor concentrations in the five soils measured by these two methods were also correlated with the IC50 values of acetochlor in wheat foliage and roots (R2 > 0.69, p ≤ 0.05). The results indicated that the CIPW and Cfree methods were effective in evaluating acetochlor toxicity to wheat and the acetochlor concentrations in wheat. The effects of soil physical and chemical properties including pH, organic matter content (OMC), clay content, and cation exchange capacity (CEC) on the acetochlor toxicity to wheat were analyzed, and soil OMC was found to be the dominant factor affecting the toxicity of acetochlor in the soil-wheat system.

Long noncoding RNA Lnc-DIF inhibits bone formation by sequestering miR-489-3p.

Osteoporosis has become a high incident bone disease along with the aging of human population. Long noncoding RNAs (LncRNAs) play an important role in osteoporosis incidence. In this study, we screened out an LncRNA negatively correlated with osteoblast differentiation, which was therefore named Lnc-DIF (differentiation inhibiting factor). Functional analysis proved that Lnc-DIF inhibited bone formation. A special structure containing multiple 53 nucleotide repeats was found in the trailing end of Lnc-DIF. Our study suggested that this repeat sequence could sequester multiple miR-489-3p and inhibit bone formation through miR-489-3p/SMAD2 axis. Moreover, siRNA of Lnc-DIF would rescue bone formation in both aging and ovariectomized osteoporosis mice. This study revealed a kind of LncRNA that could function as a sponge and regulate multiple miRNAs. RNA therapy techniques that target these LncRNAs could manipulate its downstream miRNA-target pathway with significantly higher efficiency and specificity. This provided potential therapeutic insight for RNA-based therapy for osteoporosis.

Development and field deployment of a mid-infrared CO and CO2 dual-gas sensor system for early fire detection and location.

In order to realize early fire detection and location, a mid-infrared carbon monoxide (CO) and carbon dioxide (CO2) dual-gas sensor system was developed, which mainly includes a gas pretreatment module, a CO2 sensor module, a CO sensor module, and a laptop monitoring platform. CO2 and CO absorption lines located at 4.26 µm and 4.66 µm, respectively, were selected to ensure good selectivity of the sensor system. A series of experiments were carried out to evaluate the sensor performance. The 10-90% response time of the CO and CO2 sensor modules was measured to be âˆ¼ 30 s at a flow rate of 1 L/min, and the limits of detection (LoD) of CO2 and CO were assessed to be 5.66 parts per million by volume (ppmv) and 0.94 ppmv, respectively, when the averaging time was 0.25 s. According to the correlation between CO2 and CO concentration in the early fire stage, a method of early fire detection was studied and proposed using the normalized concentration ratio between CO and CO2 (C(CO)/C(CO2)) as the key alarm parameter. Based on gas turbulent diffusion (GTD) model combined with particle swarm optimization (PSO) algorithm, a mobile early fire location method was presented. Correlative experiment results verified that the reported sensor system has a good performance for early fire detection and location.

Relationship between weather parameters and risk of exertional heat injuries during military training.

Introduction: The Singapore Armed Forces (SAF) collaborated with the Meteorological Service Singapore (MSS) to study the relationship between weather parameters and the incidents of exertional heat injury (EHI) to mitigate the risk of EHI in a practical manner. Methods: Data from the SAF's heat injury registry and MSS' meteorological data from 2012 to 2018 were used to establish a consolidated dataset of EHI incidents and same-day weather parameters rank-ordered in deciles. Poisson regression modelling was used to determine the incidence rate ratios (IRRs) of the EHI, referencing the first decile of weather parameters. Two frames of analysis were performed - the first described the relationship between the weather parameters and the adjusted IRR for the same day (D), and the second described the relationship between the weather parameters and the adjusted IRR on the following day (D + 1). Results: For wet-bulb temperature, the IRR on D + 1 approximated unity for the first nine deciles but rose to 3.09 at the tenth decile. For dew-point temperature, the IRR on D + 1 approximated unity for the first nine deciles but rose to 3.48 at the tenth decile. By designating a single dew-point temperature cut-off at ≥25.1°C (transition between the ninth and tenth decile), the adjusted IRR on D + 1 was 2.26 on days with dew-point temperature ≥25.1°C. Conclusion: Integrating the data from the SAF and MSS demonstrated that a dew-point temperature ≥25.1°C on D correlates statistically with the risk of EHI on D + 1 and could be used to supplement the risk mitigation system.

Association study between genetic variants and the risk of schizophrenia in the Chinese population based on GWAS-implicated 6p21.3-23.1 human genome region: a case-control study.

BACKGROUND: Schizophrenia is a polygenic disease; however, the specific risk genetic variants of schizophrenia are still largely unknown. Single nucleotide polymorphism (SNP) is important genetic factor for the susceptibility of schizophrenia. Investigating individual candidate gene contributing to disease risk remains important. METHODS: In a case-control study, five SNPs located in 6p21.3-p23.1 including rs2021722 in human leukocyte antigen (HLA) locus and rs107822, rs383711, rs439205 and rs421446 within the upstream of microRNA-219a-1 were genotyped in 454 schizophrenia patients and 445 healthy controls to investigate the possible association between the loci and schizophrenia in a Han Chinese population. RESULTS: Our results showed significant associations between the rs2021722 and schizophrenia in allele (A vs. G: adjusted OR = 1.661, 95%CI = 1.196-2.308), co-dominant (AG vs. GG: OR = 1.760, 95%CI = 1.234-2.510) and dominant genetic model (AG + AA vs. GG: OR = 1.756, 95%CI = 1.237-2.492), respectively. Haplotype analysis showed that TGGT and CAAC were protective factor for schizophrenia compared with TAAC haplotype (OR = 0.324, 95% CI = 0.157-0.672; OR = 0.423, 95% CI = 0.199-0.900). CONCLUSIONS: These findings indicate that rs2021722 in HLA locus might be involved in pathogenesis of schizophrenia and that genotypes AG and allele A of the locus are risk factors for schizophrenia in the Han Chinese population, confirming the association between immune system and schizophrenia.

Light-induced off-axis cavity-enhanced thermoelastic spectroscopy in the near-infrared for trace gas sensing.

A trace gas sensing technique of light-induced off-axis cavity-enhanced thermoelastic spectroscopy (OA-CETES) in the near-infrared was demonstrated by combing a high-finesse off-axis integrated cavity and a high Q-factor resonant quartz tuning fork (QTF). Sensor parameters of the cavity and QTF were optimized numerically and experimentally. As a proof-of-principle, we employed the OA-CETES for water vapor (H2O) detection using a QTF (Q-factor ∼12000 in atmospheric pressure) and a 10cm-long Fabry-Perot cavity (finesse ∼ 482). By probing a H2O line at 7306.75 cm-1, the developed OA-CETES sensor achieved a minimum detection limit (MDL) of 8.7 parts per million (ppm) for a 300 ms integration time and a normalized noise equivalent absorption (NNEA) coefficient of 4.12 × 10-9cm-1 WHz-1/2. Continuous monitoring of indoor and outdoor atmospheric H2O concentration levels was performed for verifying the sensing applicability. The realization of the proposed OA-CETES technique with compact QTF and long effective path cavity allows a class of optical sensors with low cost, high sensitivity and potential for long-distance and multi-point sensing.