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This paper presents a comprehensive study of hydrogenated amorphous silicon (a-Si)-based detectors, utilizing electrical characterization, Raman spectroscopy, photoemission, and inverse photoemission techniques. The unique properties of a-Si have sparked interest in its application for radiation detection in both physics and medicine. Although amorphous silicon (a-Si) is inherently a highly defective material, hydrogenation significantly reduces defect density, enabling its use in radiation detector devices. Spectroscopic measurements provide insights into the intricate relationship between the structure and electronic properties of a-Si, enhancing our understanding of how specific configurations, such as the choice of substrate, can markedly influence detector performance. In this study, we compare the performance of a-Si detectors deposited on two different substrates: crystalline silicon (c-Si) and flexible Kapton. Our findings suggest that detectors deposited on Kapton exhibit reduced sensitivity, despite having comparable noise and leakage current levels to those on crystalline silicon. We hypothesize that this discrepancy may be attributed to the substrate material, differences in film morphology, and/or the alignment of energy levels. Further measurements are planned to substantiate these hypotheses.

The large variation in vertebral shape and calcification observed among elasmobranch species prevents using a single method for enhancing growth bands and reading age. Further, estimating age and growth parameters can be difficult or impractical when samples are incomplete due to the bycatch of a size-selective fishery. Using a single and rapid method, age readings were obtained on the vertebrae of four batoid species, namely 53 individuals of Dasyatis pastinaca, 51 of Raja asterias, 15 of Torpedo marmorata, and 55 specimens of Torpedo torpedo, from the local small-scale trammel net fishery in the coastal waters (5–20 m depth) of the Central Tyrrhenian Sea during 2019–2021. Based on these data, a statistical routine was developed to obtain multiple estimates of age and growth parameters for incomplete samples due to size-selective fishing. The acceptable agreement between and within readers (intra and inter-reader disagreement < 5%) and the rate of increase in vertebral size with body size (differently ranked across species) demonstrated the consistency of the enhancing method. The parameters estimated by the Von Bertalanffy and Gompertz growth models matched the data available in the Mediterranean Sea for the species studied, with D. pastinaca, T. torpedo, and R. asterias showing the lowest (k = 0.05–0.12), intermediate (k = 0.112–0.19), and highest (k = 0.18–0.23) growth rates, respectively, in line with the life history traits of these species. Overall, the method proved effective both in delineating band pairs in vertebrae of different species and in reliably estimating the age and growth parameters of problematic samples due to size-selective fishing. The proposed method supports the collection of comparable demographic data from other areas where similar multi-specific assemblages are bycatch of size-selective fisheries impacting potential nursery areas and other essential habitats for elasmobranchs.

New cosmetic formulations are continuously requested by the market and the ingredients are constantly evolving. Recently the use of antioxidants has gained success and, in this context, analytical methods able to quickly and easily assess the antioxidant activity of cosmetics would make it possible to carry out analyses on new formulations even within the manufacturing process without the need for specialized laboratories and personnel, thus evaluating directly on-site the effectiveness and the shelf life of products. In this work, a chemiluminescent inhibition assay was developed for determining the total antioxidant activity in cosmetic products. The method was based on the luminol/enhancers/hydrogen peroxide/horseradish peroxidase chemiluminescent system, which generates light signals measurable through simple and compact instrumentation. The formation of the chemiluminescent signal is inhibited by the presence of antioxidant substances while it is restored once all the antioxidant molecules have been oxidized. The time of appearance of the light signal is related to the total antioxidant activity. The assay was carried out exploiting an integrated device comprising a microwell plate coupled with an array of amorphous silicon hydrogenated photosensors enclosed in a mini-dark box. The method was optimized in terms of concentrations and volumes of the required reagents and sample pre-treatment. A calibration curve was generated taking as a reference the antioxidant activity of ascorbic acid obtaining a detection limit of 10 µM. The developed method was applied to cosmetic products currently on the market as well as on spiked samples in order to evaluate the performance of the methods in terms of sensitivity, accuracy, and reproducibility.

One of the main challenges to be faced in deep space missions is to protect the health and ensure the maximum efficiency of the crew by preparing methods of prevention and in situ diagnosis. Indeed, the hostile environment causes important health problems, ranging from muscle atrophy, osteopenia, and immunological and metabolic alterations due to microgravity, to an increased risk of cancer caused by exposure to radiation. It is, therefore, necessary to provide new methods for the real-time measurement of biomarkers suitable for deepening our knowledge of the effects of space flight on the balance of the immune system and for allowing the monitoring of the astronaut’s health during long-term missions. APHRODITE will enable human space exploration because it fills this void that affects both missions in LEO and future missions to the Moon and Mars. Its scientific objectives are the design, production, testing, and in-orbit demonstration of a compact, reusable, and reconfigurable system for performing the real-time analysis of oral fluid samples in manned space missions. In the frame of this project, a crew member onboard the ISS will employ APHRODITE to measure the selected target analytes, cortisol, and dehydroepiandrosterone sulfate (DHEA-S), in oral fluid, in four (plus one additional desired session) separate experiment sessions. The paper addresses the design of the main subsystems of the analytical device and the preliminary results obtained during the first implementations of the device subsystems and testing measurements on Earth. In particular, the system design and the experiment data output of the lab-on-chip photosensors and of the front-end readout electronics are reported in detail along with preliminary chemical tests for the duplex competitive CL-immunoassay for the simultaneous detection of cortisol and DHEA-S. Different applications also on Earth are envisaged for the APHRODITE device, as it will be suitable for point-of-care testing applications (e.g., emergency medicine, bioterrorism, diagnostics in developing countries, etc.).

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR’s performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring.

Urbanization is progressing at an unprecedented rate in many places around the world. The Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions, combined with deep learning, offer new opportunities to accurately monitor urbanization at a global scale. Although the joint use of SAR and optical data has recently been investigated for urban change detection, existing data fusion methods rely heavily on the availability of sufficient training labels. Meanwhile, change detection methods addressing label scarcity are typically designed for single-sensor optical data. To overcome these limitations, we propose a semi-supervised urban change detection method that exploits unlabeled Sentinel-1 SAR and Sentinel-2 MSI data. Using bitemporal SAR and optical image pairs as inputs, the proposed multi-modal Siamese network predicts urban changes and performs built-up area segmentation for both timestamps. Additionally, we introduce a consistency loss, which penalizes inconsistent built-up area segmentation across sensor modalities on unlabeled data, leading to more robust features. To demonstrate the effectiveness of the proposed method, the SpaceNet 7 dataset, comprising multi-temporal building annotations from rapidly urbanizing areas across the globe, was enriched with Sentinel-1 SAR and Sentinel-2 MSI data. Subsequently, network performance was analyzed under label-scarce conditions by training the network on different fractions of the labeled training set. The proposed method achieved an F1 score of 0.555 when using all available training labels, and produced reasonable change detection results (F1 score of 0.491) even with as little as 10% of the labeled training data. In contrast, multi-modal supervised methods and semi-supervised methods using optical data failed to exceed an F1 score of 0.402 under this condition. Code and data are made publicly available.

Land Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic Object-Based Image Analysis (GEOBIA). In this study, we utilize very high-resolution (VHR) optical imagery with a resolution of 50 cm to improve object recognition for GEOBIA LULC classification. We focused on the city of Nice, France, and identified ten LULC classes using a Random Forest classifier in Google Earth Engine. We investigate the impact of adding Gray-Level Co-Occurrence Matrix (GLCM) texture information and spectral indices with their temporal components, such as maximum value, standard deviation, phase and amplitude from the multi-spectral and multi-temporal Sentinel-2 imagery. This work focuses on identifying which input features result in the highest increase in accuracy. The results show that adding a single VHR image improves the classification accuracy from 62.62% to 67.05%, especially when the spectral indices and temporal analysis are not included. The impact of the GLCM is similar but smaller than the VHR image. Overall, the inclusion of temporal analysis improves the classification accuracy to 74.30%. The blue band of the VHR image had the largest impact on the classification, followed by the amplitude of the green-red vegetation index and the phase of the normalized multi-band drought index.

Lab-on-Chip (LoC) devices for performing real-time PCR are advantageous compared to standard equipment since these systems allow to conduct in-field quick analysis. The development of LoCs, where the components for performing the nucleic acid amplification are all integrated, can be an issue. In this work, we present a LoC-PCR device where thermalization, temperature control and detection elements are all integrated on a single glass substrate named System-on-Glass (SoG) obtained using metal thin-film deposition. By using a microwell plate optically coupled with the SoG, real-time reverse transcriptase PCR of RNA extracted from both a plant and human virus has been carried out in the developed LoC-PCR device. The limit of detection and time of analysis for the detection of the two viruses by using the LoC-PCR were compared with those achieved by standard equipment. The results showed that the two systems can detect the same concentration of RNA; however, the LoC-PCR performs the analysis in half of the time compared to the standard thermocycler, with the advantage of the portability, leading to a point-of-care device for several diagnostic applications.

Floods are one of the most frequent natural disasters worldwide. Although the vulnerability varies from region to region, all countries are susceptible to flooding. Mozambique was hit by several cyclones in the last few decades, and in 2019, after cyclones Idai and Kenneth, the country became the first one in southern Africa to be hit by two cyclones in the same raining season. Aiming to provide the local authorities with tools to yield better responses before and after any disaster event, and to mitigate the impact and support in decision making for sustainable development, it is fundamental to continue investigating reliable methods for disaster management. In this paper, we propose a fully automated method for flood mapping in near real-time utilizing multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data acquired in the Beira municipality and Macomia district. The procedure exploits the processing capability of the Google Earth Engine (GEE) platform. We map flooded areas by finding the differences of images acquired before and after the flooding and then use Otsu’s thresholding method to automatically extract the flooded area from the difference image. To validate and compute the accuracy of the proposed technique, we compare our results with the Copernicus Emergency Management Service (Copernicus EMS) data available in the study areas. Furthermore, we investigated the use of a Sentinel-2 multi-spectral instrument (MSI) to produce a land cover (LC) map of the study area and estimate the percentage of flooded areas in each LC class. The results show that the combination of Sentinel-1 SAR and Sentinel-2 MSI data is reliable for near real-time flood mapping and damage assessment. We automatically mapped flooded areas with an overall accuracy of about 87–88% and kappa of 0.73–0.75 by directly comparing our prediction and Copernicus EMS maps. The LC classification is validated by randomly collecting over 600 points for each LC, and the overall accuracy is 90–95% with a kappa of 0.80–0.94.

AstroBio CubeSat is a mission funded by the Italian Space Agency aimed at validating novel lab-on-chip technology, that would enable the use of micro- and nanosatellites as autonomous orbiting laboratories for research in astrobiology. This 3U CubeSat is equipped with a passive magnetic attitude control system (PMACS), including permanent magnets and hysteresis strips, which allows for stabilizing the spacecraft with the longitudinal axis in the direction of the geomagnetic field vector. This work presents the process followed for the experimental characterization of the system, performed on the engineering unit of the satellite by using a Helmholtz cage facility and a spherical air-bearing to recreate environmental conditions similar to the ones experienced during the orbital motion. The hysteresis strips are characterized starting from the determination of the hysteresis loop, from which the energy dissipation per cycle and the apparent magnetic permeability are extracted. Tests performed by using the Helmholtz cage and the air-bearing facility allows for further investigating the damping torque produced by the PMACS and validating the abovementioned parameters. Numerical analysis is then used to select the number of permanent magnets which allows for achieving a pointing accuracy within an error of 10∘ within 24 h from the deployment. The analysis of the flight data supports the results obtained from the experimental test campaigns, confirming the effectiveness of the proposed methods and of the PMACS design.

In this work, we present a multifunctional Lab-on-Chip (LoC) platform based on hydrogenated amorphous silicon sensors suitable for a wide range of application in the fields of biochemical and food quality control analysis. The proposed system includes a LoC fabricated on a 5 cm × 5 cm glass substrate and a set of electronic boards for controlling the LoC functionalities. The presented Lab-on-Chip comprises light and temperature sensors, a thin film resistor acting as a heating source, and an optional thin film interferential filter suitable for fluorescence analysis. The developed electronics allows to control the thin film heater, a light source for fluorescence and absorption measurements, and the photosensors to acquire luminescent signals. All these modules are enclosed in a black metal box ensuring the portability of the whole platform. System performances have been evaluated in terms of sensor optical performances and thermal control achievements. For optical sensors, we have found a minimum number of detectable photons of 8 × 104 s−1·cm−2 at room temperature, 1.6 × 106 s−1·cm−2 in presence of fluorescence excitation source, and 2.4 × 106 s−1·cm−2 at 90 °C. From a thermal management point of view, we have obtained heating and cooling rates both equal to 2.2 °C/s, and a temperature sensor sensitivity of about 3 mV/°C even in presence of light. The achieved performances demonstrate the possibility to simultaneously use all integrated sensors and actuators, making promising the presented platform for a wide range of application fields.

Innovative materials for the integration of aptamers in Lab-on-Chip systems are important for the development of miniaturized portable devices in the field of health-care and diagnostics. Herein we highlight a general method to tailor an aptamer sequence in two subunits that are randomly immobilized into a layer of polymer brushes grown on the internal surface of microfluidic channels, optically aligned with an array of amorphous silicon photosensors for the detection of fluorescence. Our approach relies on the use of split aptamer sequences maintaining their binding affinity to the target molecule. After binding the target molecule, the fragments, separately immobilized to the brush layer, form an assembled structure that in presence of a “light switching” complex [Ru(phen)2(dppz)]2+, emit a fluorescent signal detected by the photosensors positioned underneath. The fluorescent intensity is proportional to the concentration of the target molecule. As proof of principle, we selected fragments derived from an aptamer sequence with binding affinity towards ATP. Using this assay, a limit of detection down to 0.9 µM ATP has been achieved. The sensitivity is compared with an assay where the original aptamer sequence is used. The possibility to re-use both the aptamer assays for several times is demonstrated.

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

Producing accurate land cover maps is time-consuming and estimating land cover changes between two generated maps is affected by error propagation. The increased availability of analysis-ready Earth Observation (EO) data and the access to big data analytics capabilities on Google Earth Engine (GEE) have opened the opportunities for continuous monitoring of environment changing patterns. This research proposed a framework for analyzing urban land cover change trajectories based on Landsat time series and LandTrendr, a well-known spectral-temporal segmentation algorithm for land-based disturbance and recovery detection. The framework involved the use of baseline land cover maps generated at the beginning and at the end of the considered time interval and proposed a new approach to merge the LandTrendr results using multiple indices for reconstructing dense annual land cover maps within the considered period. A supervised support vector machine (SVM) classification was first performed on the two Landsat scenes, respectively, acquired in 1987 and 2019 over Kigali, Rwanda. The resulting land cover maps were then imported in the GEE platform and used to label the interannual LandTrendr-derived changes. The changes in duration, year, and magnitude of land cover disturbance were derived from six different indices/bands using the LandTrendr algorithm. The interannual change LandTrendr results were then combined using a robust estimation procedure based on principal component analysis (PCA) for reconstructing the annual land cover change maps. The produced yearly land cover maps were assessed using validation data and the GEE-based Area Estimation and Accuracy Assessment (Area2) application. The results were used to study the Kigali’s urbanization in the last three decades since 1987. The results illustrated that from 1987 to 1998, the urbanization was characterized by slow development, with less than a 2% annual growth rate. The post-conflict period was characterized by accelerated urbanization, with a 4.5% annual growth rate, particularly from 2004 onwards due to migration flows and investment promotion in the construction industry. The five-year interval analysis from 1990 to 2019 revealed that impervious surfaces increased from 4233.5 to 12116 hectares, with a 3.7% average annual growth rate. The proposed scheme was found to be cost-effective and useful for continuously monitoring the complex urban land cover dynamics, especially in environments with EO data affordability issues, and in data-sparse regions.

Mapping Earth’s surface and its rapid changes with remotely sensed data is a crucial task to understand the impact of an increasingly urban world population on the environment. However, the impressive amount of available Earth observation data is only marginally exploited in common classifications. In this study, we use the computational power of Google Earth Engine and Google Cloud Platform to generate an oversized feature set in which we explore feature importance and analyze the influence of dimensionality reduction methods to object-based land cover classification with Support Vector Machines. We propose a methodology to extract the most relevant features and optimize an SVM classifier hyperparameters to achieve higher classification accuracy. The proposed approach is evaluated in two different urban study areas of Stockholm and Beijing. Despite different training set sizes in the two study sites, the averaged feature importance ranking showed similar results for the top-ranking features. In particular, Sentinel-2 NDVI, NDWI, and Sentinel-1 VV temporal means are the highest ranked features and the experiment results strongly indicated that the fusion of these features improved the separability between urban land cover and land use classes. Overall classification accuracies of 94% and 93% were achieved in Stockholm and Beijing study sites, respectively. The test demonstrated the viability of the methodology in a cloud-computing environment to incorporate dimensionality reduction as a key step in the land cover classification process, which we consider essential for the exploitation of the growing Earth observation big data. To encourage further research and development of reliable workflows, we share our datasets and publish the developed Google Earth Engine and Python scripts as free and open-source software.

In this work, we propose a multi-parametric sensor able to measure both temperature and radiation intensity, suitable to increase the level of integration and miniaturization in Lab-on-Chip applications. The device is based on amorphous silicon p-doped/intrinsic/n-doped thin film junction. The device is first characterized as radiation and temperature sensor independently. We found a maximum value of responsivity equal to 350 mA/W at 510 nm and temperature sensitivity equal to 3.2 mV/K. We then investigated the effects of the temperature variation on light intensity measurement and of the light intensity variation on the accuracy of the temperature measurement. We found that the temperature variation induces an error lower than 0.55 pW/K in the light intensity measurement at 550 nm when the diode is biased in short circuit condition, while an error below 1 K/µW results in the temperature measurement when a forward bias current higher than 25 µA/cm2 is applied.

There has been substantial urban growth in Stockholm, Sweden, the fastest-growing capital in Europe. The intensifying urbanization poses challenges for environmental management and sustainable development. Using Sentinel-2 and SPOT-5 imagery, this research investigates the evolution of land-cover change in Stockholm County between 2005 and 2015, and evaluates urban growth impact on protected green areas, green infrastructure and urban ecosystem service provision. One scene of 2015 Sentinel-2A multispectral instrument (MSI) and 10 scenes of 2005 SPOT-5 high-resolution instruments (HRI) imagery over Stockholm County are classified into 10 land-cover categories using object-based image analysis and a support vector machine algorithm with spectral, textural and geometric features. Reaching accuracies of approximately 90%, the classifications are then analyzed to determine impact of urban growth in Stockholm between 2005 and 2015, including land-cover change statistics, landscape-level urban ecosystem service provision bundle changes and evaluation of regional and local impact on legislatively protected areas as well as ecologically significant green infrastructure networks. The results indicate that urban areas increased by 15%, while non-urban land cover decreased by 4%. In terms of ecosystem services, changes in proximity of forest and low-density built-up areas were the main cause of lowered provision of temperature regulation, air purification and noise reduction. There was a decadal ecosystem service loss of 4.6 million USD (2015 exchange rate). Urban areas within a 200 m buffer zone around the Swedish environmental protection agency’s nature reserves increased 16%, with examples of urban areas constructed along nature reserve boundaries. Urban expansion overlapped the deciduous ecological corridor network and green wedge/core areas to a small but increasing degree, often in close proximity to weak but important green links in the landscape. Given these findings, increased conservation/restoration focus on the region’s green weak links is recommended.

The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user’s accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.

Thanks to the advances in computer power, memory storage and the availability of low-cost and high resolution digital cameras, Digital Image Correlation (DIC) is currently one of the most used optical and non-contact techniques for measuring material deformations. A free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome. Implemented in Python, the software is based on the template matching method and computes the 2D displacements and strains of samples subjected to mechanical loading. In this work, the potentialities of py2DIC were evaluated by processing two different sets of experimental data and comparing the results with other three well known DIC software packages Ncorr, Vic-2D and DICe. Moreover, an accuracy assessment was performed comparing the results with the values independently measured by a strain gauge fixed on one of the samples. The results demonstrate the possibility of successfully characterizing the deformation mechanism of the investigated materials, highlighting the pros and cons of each software package.

Lab-on-chip are analytical systems which, compared to traditional methods, offer significant reduction of sample, reagent, energy consumption and waste production. Within this framework, we report on the development and testing of an optoelectronic platform suitable for the on-chip detection of fluorescent molecules. The platform combines on a single glass substrate hydrogenated amorphous silicon photosensors and a long pass interferential filter. The design of the optoelectronic components has been carried out taking into account the spectral properties of the selected fluorescent molecule. We have chosen the [Ru(phen)2(dppz)]2+ which exhibits a high fluorescence when it is complexed with nucleic acids in double helix. The on-glass optoelectronic platform, coupled with a microfluidic network, has been tested in detection of double-stranded DNA (dsDNA) reaching a detection limit as low as 10 ng/µL.

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to “Make cities inclusive, safe, resilient and sustainable”. In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992–2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.

A lab-on-chip system, integrating an all-glass microfluidics and on-chip optical detection, was developed and tested. The microfluidic network is etched in a glass substrate, which is then sealed with a glass cover by direct bonding. Thin film amorphous silicon photosensors have been fabricated on the sealed microfluidic substrate preventing the contamination of the micro-channels. The microfluidic network is then made accessible by opening inlets and outlets just prior to the use, ensuring the sterility of the device. The entire fabrication process relies on conventional photolithographic microfabrication techniques and is suitable for low-cost mass production of the device. The lab-on-chip system has been tested by implementing a chemiluminescent biochemical reaction. The inner channel walls of the microfluidic network are chemically functionalized with a layer of polymer brushes and horseradish peroxidase is immobilized into the coated channel. The results demonstrate the successful on-chip detection of hydrogen peroxide down to 18 μM by using luminol and 4-iodophenol as enhancer agent.

In this work we present, for the first time, a rapid, compact and innovativemethod for detection of Ochratoxin A (OTA) based on hydrogenated amorphous silicon (a-Si:H) sensors. 2 μl of acidified toluene containing OTA at different concentrations werespotted on the silica side of a High Performance Thin Layer Cromatography plate andaligned with a a-Si:H p-i-n photodiode deposited by Plasma Enhanced Chemical VaporDeposition on a different glass substrate. As an UV radiation excites the mycotoxin, the re-emitted light is detected by the a-Si:H sensor. Results show a very good linearity betweenOTA concentration and the sensor photocurrent over almost three orders of magnitude. Theminimum detected OTA concentration is equal to 0.1ng, showing that the presented systemhas the potential for a low cost system suitable for the early detection of toxins in foods.

This work presents a miniaturized lab-on-chip system suitable for monitoring the activity of living cells through the on-chip detection of their bioluminescence emission. The system integrates amorphous silicon diodes, acting as temperature and light sensors, and indium tin oxide film, acting as heater, on a single glass substrate. During its operation, the glass is thermally and optically coupled to the investigated cells and electrically connected to an electronic board, which controls the lab-on-chip temperature and monitors the sensor photocurrents. The proposed lab-on-chip is particularly attractive for the development of portable cell-based biosensors useful for biological activity monitoring as well as for cell cytotoxicity evaluation.

This paper presents a thin film structure suitable for low-level radiation measurements in lab-on-chip systems that are subject to thermal treatments of the analyte and/or to large temperature variations. The device is the series connection of two amorphous silicon/amorphous silicon carbide heterojunctions designed to perform differential current measurements. The two diodes experience the same temperature, while only one is exposed to the incident radiation. Under these conditions, temperature and light are the common and differential mode signals, respectively. A proper electrical connection reads the differential current of the two diodes (ideally the photocurrent) as the output signal. The experimental characterization shows the benefits of the differential structure in minimizing the temperature effects with respect to a single diode operation. In particular, when the temperature varies from 23 to 50 °C, the proposed device shows a common mode rejection ratio up to 24 dB and reduces of a factor of three the error in detecting very low-intensity light signals.