This work focuses on the characterization of the complex dielectric function of a polymer material, which is UV-cured dielectric ink 1092, used in the DragonFly IV 3D inkjet printer. Infrared spectroscopic ellipsometry was performed over the spectral range of 300–4000 cm−1 at multiple angles of incidence to extract both real and imaginary components of the dielectric response. In addition, polarized transmission measurements were taken over the spectral range from 300–6000 cm−1 to aid in characterization. We report an isotropic dielectric function model that is composed of oscillators with both Gaussian and Lorentzian broadening. This model reveals strong absorption bands at 925–1500 cm−1, 1600–1775cm−1, and 2840–3000 cm−1 while otherwise appearing largely transparent. This parameterized dielectric function is critical in first-principles modeling of infrared optical components and metamaterials fabricated using this polymer.

This study examines the relationship between the wingbeat frequency of flying insects and ambient temperature, leveraging data from over 302,000 insect observations obtained using a near-infrared optical sensor during an eight-month field experiment. By measuring the wingbeat frequency as well as wing and body optical cross-sections of each insect in conjunction with the ambient temperature, we identified five clusters of insects and analyzed how their average wingbeat frequencies evolved over temperatures ranging from 10 °C to 38 °C. Our findings reveal a positive correlation between temperature and wingbeat frequency, with a more pronounced increase observed at higher wingbeat frequencies. Frequencies increased on average by 2.02 Hz/°C at 50 Hz, and up to 9.63 Hz/°C at 525 Hz, and a general model is proposed. This model offers a valuable tool for correcting wingbeat frequencies with temperature, enhancing the accuracy of insect clustering by optical and acoustic sensors. While this approach does not account for species-specific responses to temperature changes, our research provides a general insight, based on all species present during the field experiment, into the intricate dynamics of insect flight behavior in relation to environmental factors.

Optical sensors have shown significant promise in offering additional data to track insect populations. This article presents a comparative study between abundance measurements obtained from a novel near-infrared optical sensor and physical traps. The optical instrument, named an Entomological Bistatic Optical Sensor System, or eBoss, is a non-destructive sensor operating in the near-infrared spectral range and designed to continuously monitor the population of flying insects. The research compares the mosquito aerial density (#/m3) obtained through the eBoss with trap counts from eight physical traps during an eight-month field study. The eBoss recorded over 302,000 insect sightings and assessed the aerial density of all airborne insects as well as male and female mosquitoes specifically with a resolution of one minute. This capability allows for monitoring population trends throughout the season as well as daily activity peaks. The results affirmed the correlation between the two methods. While optical instruments do not match traps in terms of taxonomic accuracy, the eBoss offered greater temporal resolution (one minute versus roughly three days) and statistical significance owing to its much larger sample size. These outcomes further indicate that entomological optical sensors can provide valuable complementary data to more common methods to monitor flying insect populations, such as mosquitoes or pollinators.

Monitoring the dynamics of insect populations is key to assessing the impact of human activities on insect populations. However, traditional methodologies relying on physical traps have inherent limitations in accurately monitoring insect abundance. Here, we present findings from a 9-month campaign conducted in New Jersey, USA, utilizing a near-infrared optical sensor known as eBoss. From April to December 2022, the eBoss derived the aerial density (insect/m 3 ) and biomass density (mg/m 3 ) with a 1-min resolution from a total of 302,093 insect observations. The data collected were analyzed in relation to air temperature, relative humidity, and wind speed. The results revealed that the abundance of flying insects exhibited an initial increase from April to June, reaching a peak of 0.094 insect/m 3 and 1.34 mg/m 3 , followed by a subsequent decline towards the end of the year. Our investigation showed a surge in insect abundance above 12.5 °C, with particularly high levels observed between 19 and 31 °C. The impact of relative humidity and wind speed on insect populations was also explored. Overall, this campaign demonstrated the efficacy of photonic sensors in gathering novel and extensive data for the field of entomology, paving the way for improved understanding and management of insect populations.

Insects are major actors in Earth's ecosystems and their recent decline in abundance and diversity is alarming. The monitoring of insects is paramount to understand the cause of this decline and guide conservation policies. In this contribution, an infrared laser-based system is used to remotely monitor the biomass density of flying insects in the wild. By measuring the optical extinction caused by insects crossing the 36-m long laser beam, the Entomological Bistatic Optical Sensor System used in this study can evaluate the mass of each specimen. At the field location, between July and December 2021, the instrument made a total of 262,870 observations of insects for which the average dry mass was 17.1 mg and the median 3.4 mg. The daily average mass of flying insects per meter cube of air at the field location has been retrieved throughout the season and ranged between near 0 to 1.2 mg/m3. Thanks to its temporal resolution in the minute range, daily variations of biomass density have been observed as well. These measurements show daily activity patterns changing with the season, as large increases in biomass density were evident around sunset and sunrise during Summer but not during Fall.

New methodologies based on photonic sensors to survey flying insect populations in their natural habitat have gained traction over the last decade. Instruments such as entomological lidars, radars, or standoff sensors using active or passive light sources can observe large numbers of insects transiting through their field of view. While counting transits can inform on the relative change in insect population, it does not inform on the absolute population size, nor allows data from different instruments to be compared with one another. We propose a methodology to convert transit counts into an absolute insect population density, expressed in number of flying insects per meter cube. The latter benefits from being independent from the instrument and experiment characteristics, making it an ideal metric to compare data originating from different sources. Due to the stochastic nature of the process, the uncertainty of the retrieved population density varies with its temporal resolution, the volume of air probed by the instrument and the population density itself. To study these complex relationships, a numerical model simulating the interactions of insects with a photonic sensor is presented. Finally, we offer an empirical solution to describe the relationship between the population density temporal resolution and its uncertainty.

Although small in size, insects are a quintessential part of terrestrial ecosystems due to their large number and diversity. While captured insects can be thoroughly studied in laboratory conditions, their population dynamics and abundance in the wild remain largely unknown due to the lack of accurate methodologies to count them. Here, we present the results of a field experiment where the activity of insects has been monitored continuously over 3 months using an entomological stand-off optical sensor (ESOS). Because its near-infrared laser is imperceptible to insects, the instrument provides an unbiased and absolute measurement of the aerial density (flying insect/m3) with a temporal resolution down to the minute. Multiple clusters of insects are differentiated based on their wingbeat frequency and ratios between wing and body optical cross-sections. The collected data allowed for the study of the circadian rhythm and daily activities as well as the aerial density dynamic over the whole campaign for each cluster individually. These measurements have been compared with traps for validation of this new methodology. We believe that this new type of data can unlock many of the current limitations in the collection of entomological data, especially when studying the population dynamics of insects with large impacts on our society, such as pollinators or vectors of infectious diseases.

The surveillance of mosquito populations is paramount in the fight against mosquito-borne diseases that affect millions of people every year. Evaluating the efficiency of mitigation methods requires extensive and long-term surveys which can be costly and time consuming. The recent development of optical sensors give access to alternative methods for entomological monitoring but require efficient classification algorithms to be successful. In this contribution, supervised machine learning algorithms such as Linear Discriminant Analysis, Decision Trees, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes are compared for the identification of mosquitoes through optical signals. Based on predictor variables derived from the wing beat frequency and optical cross section of mosquitoes, these algorithms were trained to perform different classification tasks: the identification of species, sex and/or gravidity of mosquitoes present in New Jersey, USA. Results shows that the most polyvalent machine learning algorithm for mosquito identification is Support Vector Machine that performs on average over all tasks between 0.65 and 7.3% better than other algorithms. Moreover, Support Vector Machine is the algorithm that best performed for the most complex tasks, more than 2% above the second best, it is therefore the most suited for real-world study where several species of mosquito can be expected at a single location. A close second is Linear Discriminant Analysis that is only outperformed by Support Vector Machine by 0.65% over all tasks and is the most performant when studying mosquito gravidity. Finally, Decision Trees algorithm has reached almost perfection in identifying the sex of a single mosquito species with 99.9% accuracy which is 1.3% more than the second best performing algorithm on this task. These results demonstrate that optical sensors, coupled with machine learning, can be a viable alternative or complementary methodology for the monitoring of mosquito populations. Furthermore, this methodology can perform non-intrusive, automatic, time-resolved measurements of insect population dynamics over extended periods of time without the need for laboratory analysis of captured specimens as in most traditional survey methods.

Improving the survey of mosquito populations is of the utmost importance to further enhance mitigation techniques that protect human populations from mosquito-borne diseases. While mosquito populations are generally studied using physical traps, stand-off optical sensors allow to study insect ecosystems with potentially better spatial and temporal resolution. This can be greatly beneficial to eco-epidemiological models and various mosquito control programs. In this contribution, we demonstrate that the gravidity of female mosquitoes can be identified from changes in their spectral and polarimetric backscatter cross sections. Among other predictive variables, the wing beat frequency and the depolarization ratio of the mosquito body allows for the identification of gravid females with a precision and recall of 86% and 87%, respectively. Since female mosquitoes need a blood meal to become gravid, statistics on gravidity is of prime importance as only females that have been gravid might carry infectious diseases. In addition, it allows to detect possible breeding habitat, predict a potential increase in the mosquito population and provide a better overall understanding of the ecosystem dynamics. As a result, targeted and localized mitigation techniques can be used, reducing the cost and improving the efficiency of mosquito population control.

Mosquito-borne diseases are a major challenge for Human health as they affect nearly 700 million people every year and result in over 1 million deaths. Reliable information on the evolution of population and spatial distribution of key insects species is of major importance in the development of eco-epidemiologic models. This paper reports on the remote characterization of flying mosquitoes using a continuous-wave infrared optical remote sensing system. The system is setup in a controlled environment to mimic long-range lidars, mosquitoes are free flying at a distance of ~ 4 m from the collecting optics. The wing beat frequency is retrieved from the backscattered light from mosquitoes transiting through the laser beam. A total of 427 transit signals have been recorded from three mosquito species, males and females. Since the mosquito species and gender are known a priori, we investigate the use of wing beat frequency as the sole predictor variable for two Bayesian classifications: gender alone (two classes) and species/gender (six classes). The gender of each mosquito is retrieved with a 96.5% accuracy while the species/gender of mosquitoes is retrieved with a 62.3% accuracy. Known to be an efficient mean to identify insect family, we discuss the limitations of using wing beat frequency alone to identify insect species.

Mosquito-borne diseases are a major challenge for Human health as they affect nearly 700 million people every year. Monitoring insects is generally done through trapping methods that are tedious to set up, costly and present scientific biases. Entomological lidars are a potential solution to remotely count and identify mosquito species and gender in realtime. In this contribution, a dual-wavelength polarization sensitive lidar is used in laboratory conditions to retrieve the wingbeat frequency as well as optical properties of flying mosquitoes transiting through the laser beam. From the lidar signals, predictive variables are retrieved and used in a Bayesian classification. This paper focuses on determining the relative importance of the predictive variables used in the classification. Results show a strong dominance of the wingbeat frequency, the impact of predictive variables based on depolarization and backscattering ratios are discussed, showing a significant increase in classification accuracy.

Hard-target lidars rely on the reflectivity and backscattering properties of topographic targets, which are rather difficult to evaluate, resulting in uncertainties when assessing the performance of the instrument. In this work, backscattering properties and hemispherical reflectance of topographic targets are measured in the visible, near-infrared, and mid-infrared spectral ranges. A laboratory setup mimicking a hard-target lidar is used to measure the backscattered signals at various angles of incidence, which are then fitted using a bidirectional reflectance distribution function Phong model. We show that these results are useful for optimizing active stand-off detection and hard-target lidars and for increasing their overall efficiency.

Active stand-off detection and hard-target lidars are common methodologies for gas identification, chemical emission tracing, hazardous material sensing, or explosive detection to name a few. By their nature, this type of instrument heavily relies on the reflectivity or backscattering properties of distant targets. While some applications allow the use of retroreflectors, most mobile systems require the use of actual topographic targets, such as the ground, roads, buildings, roofs, or vegetation. In this work, N2O path-averaged mixing ratios are measured with the 10 Hz frequency using a quantum cascade laser open path system operating at 7.7 μm wavelength. Measurements are performed by detecting the light backscattered from common topographic targets located 5.5 m away from the instrument. For each topographic target, the detection limit and accuracy of the retrieved mixing ratios are presented and discussed showing detection limits between 0.008 and 1.36 ppm depending on the target and mixing ratio relative errors between 4 and 80 %.

Fugitive gas emissions from agricultural or industrial plants and gas pipelines are an important environmental concern as they contribute to the global increase of greenhouse gas concentrations. Moreover, they are also a security and safety concern because of possible risk of fire/explosion or toxicity. This study presents standoff detection of CH4 and N2O leaks using a quantum cascade laser open-path system that retrieves path-averaged concentrations by collecting the backscattered light from a remote hard target. It is a true standoff system and differs from other open-path systems that are deployed as point samplers or long-path transmission systems that use retroreflectors. The measured absorption spectra are obtained using a thermal intra-pulse frequency chirped DFB quantum cascade laser at ~7.7 µm wavelength range with ~200 ns pulse width. Making fast time resolved observations, the system simultaneously realizes high spectral resolution and range to the target, resulting in path-averaged concentration retrieval. The system performs measurements at high speed ~15 Hz and sufficient range (up to 45 m, ~148 feet) achieving an uncertainty of 3.1 % and normalized sensitivity of 3.3 ppm m Hz−1/2 for N2O and 9.3 % and normalized sensitivity of 30 ppm m Hz−1/2 for CH4 with a 0.31 mW average power QCL. Given these characteristics, this system is promising for mobile or multidirectional search and remote detection of gas leaks.

In this contribution, we present recent work on the ability to achieve range-resolved greenhouse gases concentration measurements in the Earth’s atmosphere (CH4 , H2 O) by combining broadband optical correlation spectroscopy (OCS) with lidar. We show that OCS-Lidar is a robust methodology, allowing trace gases remote sensing with a low dependence on the temperature and pressure-variation absorption cross section. Moreover, we evaluate, as an experimental proof, the water vapor profile in the planetary boundary layer using the 4ν 720 nm absorption band.

Fugitive gas emissions from agricultural or industrial plants and gas pipelines are an important environmental concern as they can contribute to the global increase of greenhouse gas concentration. Moreover, they are also a security and safety concern because of possible risk of fire/explosion or toxicity. This study presents gas concentration measurements using a quantum cascade laser open path system (QCLOPS). The system retrieves the pathaveraged concentration of N2O and CH4 by collecting the backscattered light from a scattering target. The gas concentration measurements have a high temporal resolution (68 ms) and are achieved at sufficient range (up to 40 m, ~ 130 feet) with a detection limit of 2.6 ppm CH4 and 0.4 ppm for N2O. Given these characteristics, this system is promising for mobile/multidirectional remote detection and evaluation of gas leaks. The instrument is monostatic with a tunable QCL emitting at ~ 7.7 μm wavelength range. The backscattered radiation is collected by a Newtonian telescope and focused on an infrared light detector. Puffs of N2O and CH4 are released along the optical path to simulate a gas leak. The measured absorption spectrum is obtained using the thermal intra-pulse frequency chirped DFB QCL and is analyzed to obtain path averaged gas concentrations.

Identifying and quantifying ambient aerosols and their interactions with clouds are important for air-quality and climate studies. Advances in infrared technologies on fiber lasers, quantum cascade lasers and IR detectors have made developing micro-pulse (low energy) IR lidar systems operating in the infrared spectral range feasible. We present in this contribution a micropulse dual channel (IR wavelength) lidar system for studying aerosol and cloud optical properties. The system operates at 1.545 μm (6472.5 cm-1) and at 4.55 μm (2197.8 cm-1) with high repetition rates and microjoule pulses. The system is intended to be coupled with an existing UV, visible, near infrared lidar system at the city college of New York, part of the CREST lidar network Preliminary backscattered signals from this system are here presented and compared to SNR simulation.

Methane and Nitrous Oxide are long-lived greenhouse gases in the atmosphere with significant global warming effects. We report on application of chirped-pulsed quantum cascade lasers (QCLs) to simultaneous measurements of these trace gases in both open-path fence-line and backscatter systems. The intra-pulse thermal frequency chirp in a QCL can be time resolved and calibrated to allow for high resolution differential optical absorption spectroscopy over the spectral window of the chirp, which for a DFB-QCL can be reach similar to 2cm(-1) for a 500 nsec pulse. The spectral line-shape of the output from these lasers are highly stable from pulse to pulse over long period of time (>1 day), and the system does not require frequent calibrations.

Fugitive gas emissions from agricultural or industrial plants and gas pipelines are an important environmental concern as they can contribute to the global increase of greenhouse gas concentration. Moreover, they are also a security and safety concern because of possible risk of fire/explosion or toxicity. This study presents gas concentration measurements using a quantum cascade laser open path system (QCLOPS). The system retrieves the path-averaged concentration of N2O by collecting the backscattered light from a scattering target. The gas concentration measurements have a high temporal resolution (68 ms) and are achieved at sufficient range (up to 40 m, ~ 130 feet) with a detection limit of 0.4 ppm for N2O. Given these characteristics, this system is promising for mobile/multidirectional remote detection and evaluation of gas leaks.

Understanding new particles formation in the free troposphere is key for air quality and climate change, but requires accurate observation tools. Here, we discuss on the optical requirements ensuring a backscattering device, such as a lidar, to remotely observe nucleation events promoted by nonspherical desert dust or volcanic ash particles. By applying the Mie theory and the T-matrix code, we numerically simulated the backscattering coefficient of spherical freshly nucleated particles and nonspherical particles. We hence showed that, to remotely observe such nucleation events with an elastic lidar device, it should operate in the UV spectral range and be polarization-resolved. Two atmospheric case studies are proposed, on nucleation events promoted by desert dust, or volcanic ash particles. This optical pathway might be useful for climate, geophysical and fundamental purposes, by providing a range-resolved remote observation of nucleation events.