Change Detection Research Articles

1H-NMR Spectroscopy of Blood Plasma for Detection of Changes in Metabolism during the Development of Sarcoma M-1 in Rats

1H-NMR Spectroscopy of Blood Plasma for Detection of Changes in Metabolism during the Development of Sarcoma M-1 in Rats

Advantages of using T1-mapping in cardiac magnetic resonance imaging in patients with acromegaly

Aim. To determine the incidence of interstitial myocardial fibrosis (MF) in acromegaly, which leads to the development of cardiac arrhythmias and conduction disorders. Materials and methods. A single-center study was conducted, including 70 patients with acromegaly. All patients underwent standard medical examinations, including hormonal blood tests, electrocardiograms, echocardiography, Holter monitoring electrocardiograms and magnetic resonance imaging (MRI) of the heart with gadolinium contrast, additionally 48 patients underwent T1-mapping, which is an MRI method that allows the detection of diffuse changes by measuring the values of myocardial relaxation time – T1. Results. The study revealed a high incidence of MF – 53 (75.7%) cases (21 women, 32 men). The duration of the disease was found to be critical for myocardial remodeling (p=0.006). Compliance criteria for the T1-mapping technique were determined, with reduced T1-mapping values observed in 85.2% of cases. The ROC analysis (Receiver Operator Characteristic) determined the diagnostic value of post-contrast T1-mapping: AUC (Area Under the Curve)=0.906 (95% confidence interval 0.789–1.000), a sensitivity of 90%, and a specificity of 76.5%, indicating high diagnostic efficiency. According to the Youden index, a cut-off point of 372.5 ms was selected. Conclusion. Myocardial T1-mapping is a novel MRI method that allows to assess the degree of MF without the need for myocardial biopsy. The obtained information is crucial for the diagnosis and prognostic assessment of heart diseases, particularly in cases of hypertrophic cardiomyopathy or infiltrative diseases. The T1-mapping method, which is actively developing, can serve as a marker for early diffuse myocardial fibrosis and help determine the prognosis for patients with acromegalic cardiomyopathy.

Click Coupling of Flavylium Dyes with Plasmodione Analogues: Towards New Redox-Sensitive Pro-Fluorophores.

The development of redox-sensitive molecular fluorescent probes for the detection of redox changes in Plasmodium falciparum-parasitized red blood cells remains of interest due to the limitations of current genetically encoded biosensors. This study describes the design, screening and synthesis of new pro-fluorophores based on flavylium azido dyes coupled by click chemistry to alkynyl analogues of plasmodione oxide, the key metabolite of the potent redox-active antimalarial plasmodione. The photophysical and electrochemical properties of these probes were evaluated, focusing on their fluorogenic responses. The influence of the redox status of the quinone and the length of the PEG chain separating the fluorophore from the electrophore on the photophysical properties was investigated. The fluorescence quenching by photoinduced electron transfer is reversible and of high amplitude for probes in oxidized quinone forms and fluorescence is reinstated for reduced hydroquinone forms. Our results demonstrate that shortening the PEG chain has the effect of enhancing the fluorogenic response, likely due to non-covalent interactions between the two chromophores. All these systems were evaluated for their antiparasitic activities and fluorescence imaging suggests the efficacy of the fluorescent flavylium dyes in P. falciparum-parasitized red blood cells, paving the way for future parasite imaging studies to monitor cellular redox processes.

Prediction of Nursing Need Proxies Using Vital Signs and Biomarkers Data: Application of Deep Learning Models.

To develop deep learning models to predict nursing need proxies among hospitalised patients and compare their predictive efficacy to that of a traditional regression model. This methodological study employed a cross-sectional secondary data analysis. This study used de-identified electronic health records data from 20,855 adult patients aged 20 years or older, admitted to the general wards at a tertiary hospital. The models utilised patient information covering the preceding 2 days, comprising vital signs, biomarkers and demographic data. To create nursing need proxies, we identified the six highest-workload nursing tasks. We structured the collected data sequentially to facilitate processing via recurrent neural network (RNN) and long short-term memory (LSTM) algorithms. The STROBE checklist for cross-sectional studies was used for reporting. Both the RNN and LSTM predicted nursing need proxies more effectively than the traditional regression model. However, upon testing the models using a sample case dataset, we observed a notable reduction in prediction accuracy during periods marked by rapid change. The RNN and LSTM, which enhanced predictive performance for nursing needs, were developed using iterative learning processes. The RNN and LSTM demonstrated predictive capabilities superior to the traditional multiple regression model for nursing need proxies. Applying these predictive models in clinical settings where medical care complexity and diversity are increasing could substantially mitigate the uncertainties inherent in decision-making processes. We used de-identified electronic health record data of 20,855 adult patients about vital signs, biomarkers and nursing activities. The authors state that they have adhered to relevant EQUATOR guidelines: STROBE statement for cross-sectional studies. Despite widespread adoption of deep learning algorithms in various industries, their application in nursing administration for workload distribution and staffing adequacy remains limited. This study amalgamated deep learning technology to develop a predictive model to proactively forecast nursing need proxies. Our study demonstrates that both the RNN and LSTM models outperform a traditional regression model in predicting nursing need proxies. The proactive application of deep learning methods for nursing need prediction could help facilitate timely detection of changes in patient nursing demands, enabling the effective and safe nursing services.

Cost-effectiveness of remote sensing technology for spruce budworm monitoring in Maine, USA

Forest pests are a major disturbance factor in forest ecosystems, which can result in tree mortality and loss of ecosystem services, leading to further negative impacts on the forest economy. Spruce budworm (Choristoneura fumiferana (Clem.); SBW) is a native forest pest in the northeastern USA and Canada, including the state of Maine, which defoliates balsam fir (Abies balsamea (L.) Mill.) and spruce (Picea spp.) trees with cyclical outbreaks every 30-60 years. SBW is typically monitored via ground sampling techniques such as pheromone traps and overwintering second instar larvae (L2) branch sampling. Remote sensing data can also provide information about defoliation patterns across the landscape and forest susceptibility to outbreaks. This study presents a cost-effectiveness analysis comparing remote sensing data, ground sampling techniques, and an integrated monitoring approach, combining remote sensing change detection with field sampling. Over a 10-year project period, Sentinel-2 imagery emerged as the most cost-effective option, ranging from US$33 to US$63/square kilometer (sq km), offering wide spatial coverage and moderate resolution suitable for the identification of defoliation patterns. PlanetScope imagery ranged from US$77 to US$241/sq km, and unmanned aerial vehicle (UAV) imagery had the greatest variation, from US$9,220 to US$58,481/sq km. Labor costs are the most influential in our study, ranging from 30% of total costs for remote sensing approaches to 80% for field sampling. The integrated monitoring approach proposed in this study presents a synergistic strategy for effective and timely SBW monitoring, ranging from US$144 to US$213/sq km. Utilizing this integrated approach leverages both remote sensing and L2 branch surveys to enhance the accuracy and timeliness of monitoring efforts, leading to more effective management strategies for mitigating pest outbreaks for landowners. Our research highlights the importance of adaptive monitoring strategies and integrating remote sensing for forest pest detection.

Performance evaluation of CXCL10 ELISA “cosmic” kit to measure CXCL10 in cerebrospinal fluid of patients with HTLV‐1‐associated myelopathy

AbstractObjectivesThis study aimed to validate the clinical utility of cerebrospinal fluid (CSF) CXCL10 measurements in HTLV‐1‐associated myelopathy (HAM) using a CXCL10 ELISA “Cosmic” kit, a more widely applicable method than cytometric bead array (CBA).MethodsCSF CXCL10 levels were measured in 165 samples from 111 patients with HAM and 18 controls using a CXCL10 ELISA “Cosmic” kit. We assessed the following: (1) CSF CXCL10 concentrations by HAM activity level (high, moderate, and low) versus controls; (2) correlation with CBA; (3) cutoff values, sensitivity, and specificity for differentiating among HAM activity levels; (4) changes in HAM activity after steroid therapy; and (5) relationship between HAM activity and prognosis in patients undergoing steroid therapy. A correlation coefficient of ≥0.9 with CBA was the primary endpoint.ResultsThe median CSF CXCL10 levels in the high, moderate, low, and control groups were 4016.0, 841.0, 112.8, and 102.5 pg/mL, respectively. The ELISA findings were highly correlated with the CBA findings (r = 0.99). Cutoff values were set at 2500 pg/mL (sensitivity, 93.3%; specificity, 100%) to distinguish between high and moderate activity and 180 pg/mL (sensitivity, 81.8%; specificity, 100%) for low to moderate activity comparable to CBA. The new cutoffs enabled the detection of HAM activity changes and prediction of motor disability progression under steroid therapy.ConclusionCXCL10 ELISA “Cosmic” kit findings were strongly correlated with CBA findings, meeting the primary endpoint and demonstrating comparable sensitivity and specificity for distinguishing HAM activity. This product shows a promising ability to determine the therapeutic strategy.

A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery

Aboveground biomass (AGB) estimates derived from Landsat’s spectral bands are limited by spectral saturation when AGB densities exceed 150–300 Mg ha−1. Statistical features that characterize image texture have been proposed as a means to alleviate spectral saturation. However, apart from Gray Level Co-occurrence Matrix (GLCM) statistics, many spatial feature engineering techniques (e.g., morphological operations or edge detectors) have not been evaluated in the context of forest AGB estimation. Moreover, many prior investigations have been constrained by limited geographic domains and sample sizes. We utilize 176 lidar-derived AGB maps covering ∼9.3 million ha of forests in the Pacific Northwest of the United States to construct an expansive AGB modeling dataset that spans numerous biophysical gradients and contains AGB densities exceeding 1000 Mg ha−1. We conduct a large-scale inter-comparison of multiple spatial feature engineering techniques, including GLCMs, edge detectors, morphological operations, spatial buffers, neighborhood vectorization, and neighborhood similarity features. Our numerical experiments indicate that statistical features derived from GLCMs and spatial buffers yield the greatest improvement in AGB model performance out of the spatial feature engineering strategies considered. Including spatial features in Random Forest AGB models reduces the root mean squared error (RMSE) by 9.97 Mg ha−1. We contextualize this improvement model performance by comparing to AGB models developed with multi-temporal features derived from the LandTrendr and Continuous Change Detection and Classification algorithms. The inclusion of temporal features reduces the model RMSE by 18.41 Mg ha−1. When spatial and temporal features are both included in the model’s feature set, the RMSE decreases by 21.71 Mg ha−1. We conclude that spatial feature engineering strategies can yield nominal gains in model performance. However, this improvement came at the cost of increased model prediction bias.

Monthly Prediction of Pine Stress Probability Caused by Pine Shoot Beetle Infestation Using Sentinel-2 Satellite Data

Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to address this challenge. First, representative pine tree stress samples were selected by combining long-term forest disturbance data using the Continuous Change Detection and Classification (CCDC) algorithm with high-resolution remote sensing imagery. Monthly cloud-free Sentinel-2 images were then composited using the Multifactor Weighting (MFW) method. Finally, a Random Forest (RF) algorithm was employed to build the pine tree stress probability model and analyze the importance of spectral, topographic, and meteorological features. The model achieved prediction precisions of 0.876, 0.900, and 0.883, and overall accuracies of 89.5%, 91.6%, and 90.2% for January, February, and March 2023, respectively. The results indicate that spectral features, such as band reflectance and vegetation indices, ranked among the top five in importance (i.e., SWIR2, SWIR1, Red band, NDVI, and NBR). They more effectively reflected changes in canopy pigments and leaf moisture content under stress compared with topographic and meteorological features. Additionally, combining long-term stress disturbance data with high-resolution imagery to select training samples improved their spatial and temporal representativeness, enhancing the model’s predictive capability. This approach provides valuable insights for improving forest health monitoring and uncovers opportunities to predict future beetle outbreaks and take preventive measures.

Investigating the potential of ICEYE-SAR data in storm damage detection in a coniferous forest with rugged terrain

ABSTRACT Adaptive forest management strategies require accurate detection of forest disturbance, in various spatial scales. Synthetic Aperture Radar (SAR) data can provide information about the forest attributes, penetrating the canopy at different levels under any weather and lighting conditions. ICEYE consists of the largest constellation of SAR satellites, enabling very high spatial and temporal resolution. In this study, ICEYE data were investigated in the detection of storm damage, in a fir forest with complex topography which was recently hit by the Daniel storm, resulting in severe damage to the forest structure (FS) and alluvium depositions (AD). To identify the best potential for storm damage detection using ICEYE data, an unsupervised change detection approach was employed combining wavelet transform and adaptive thresholds at spatial scales of 0.5 m (R05), 1 m (R1), 2 m (R2) and 3 m (R3). Additionally, two morphological filters were applied in best-performing resolutions to assess the impact of post-processing on the detection accuracy. Finally, FS and AD damage were investigated separately in order to provide detailed information about the detection capabilities of ICEYE. The results showcased that R1 (UA = 32.35, PA = 18.34 and K = 0.31) provided the best detection performance, followed by R05 (UA = 20.62, PA = 20.12 and K = 0.19). Furthermore, the employment of morphological filters slightly increased UA and Kappa metrics in both R1(UA = 33.40 and K = 0.32) and R05 (UA = 25.60 and K = 0.24), suggesting that post-processing is necessary to mitigate false detections. Regarding the investigation of AD and FS damage, it was revealed that post-processed ICEYE data in both R05 and R1 are capable of identifying AD with satisfying accuracy (UA = 47.41, PA = 22.36, K = 0.47), while FS damage detection is more challenging (UA = 10.15%-14.40%, PA = 14.55%-15.11%, and Kappa = 0.10-0.17). Overall, this study demonstrated that ICEYE can be used to detect storm-affected areas in mountainous forest ecosystems, especially in cases where detection with other methods is not feasible.

JointNet4BCD: A Semi-Supervised Joint Learning Neural Network with Decision Fusion for Building Change Detection

Remote sensing image building change detection aims to identify building changes that occur in remote sensing images of the same areas acquired at different times. In recent years, the development of deep learning has led to significant advancements in building change detection methods. However, these fully supervised methods require a large number of bi-temporal remote sensing images with pixel-wise change detection labels to train the model, which incurs substantial time and manpower for annotation. To address this issue, this study proposes a novel single-temporal semi-supervised joint learning framework for building change detection, called JointNet4BCD. Firstly, to reduce annotation costs, we design a semi-supervised learning manner to train our model using a small number of building extraction labels instead of a large amount of building change detection labels. Furthermore, to improve the semantic understanding capability of the model, we propose a joint learning approach for building extraction and change detection tasks. Lastly, a decision fusion block is designed to fuse the building extraction results into the building change detection results to further improve the accuracy of building change detection. Experimental results on the two widely used datasets demonstrate that the proposed JointNet4BCD achieves excellent building change detection performance while reducing the need for labels from thousands to dozens. Using only ten labeled images, JointNet4BCD achieved F1-Scores of 83.93% and 83.45% on the LEVIR2000 and WHU datasets, respectively.

Enhanced Surface Plasmon Resonance Sensing via Kramers-Kronig Phase Extraction.

This study introduces a novel approach to enhance surface plasmon resonance (SPR) sensing using Kramers-Kronig (K-K) relations for phase extraction from reflection spectra. By applying the K-K relations, a phase shift sensitivity of 3400°/RIU is achieved improving SPR detection limits to 9 × 10-6 RIU, which is four times more sensitive than conventional methods for determining SPR peak shifts. This advancement enables more precise detection of refractive index changes in aqueous solutions, making it valuable for biosensing and environmental monitoring with spectrometers.

Comparative Trend Analysis of Precipitation Indices in Several Towns of the Sirba River Catchment (Burkina Faso) from CHIRPS and TAMSAT Rainfall Estimates

The increasingly frequent pluvial flood of West African urban settlements indicates the need to investigate the drivers of local rainfall changes. However, meteorological stations are few, unevenly distributed, and work irregularly. Daily satellite rainfall datasets can be used. Nevertheless, these products often need to be more accurate due to sensor errors and limitations in retrieval algorithms. The problem is, therefore, how to characterize rainfall where there is a need for ground-based rainfall records or incomplete series. This study aims to characterize urban rainfall using two satellite datasets. The analysis was carried out in the Sirba river catchment, Burkina Faso, using the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Tropical Applications of Meteorology using SATellite and ground-based data (TAMSAT) datasets. Ten indices from the Expert Team on Climate Change Detection and Indices (ETCCDI) of precipitation were calculated, and their statistical trends were evaluated from 1983 to 2023. The study introduces two key innovations: a comparative analysis of precipitation trends using two satellite datasets and applying this analysis to towns within a previously understudied 39,138 km2 catchment area that is frequently flooded. Both datasets agree on the increase of (i) annual cumulative rainfall over all towns, (ii) five-day maximum rainfall over the town of Manni, (iii) rainfall due to very wet days in Gayéri, (iv) days of heavy rainfall in Bogandé, Manni and Yalgho, and (v) days of very heavy rainfall in Yalgho. These findings suggest the need for targeted pluvial flood prevention measures in towns with increasing trends in heavy rainfall.

Linking Land Use Change and Hydrological Responses: The Role of Agriculture in the Decline of Urmia Lake

The water level and surface area of Urmia Lake, located in the northwest of Iran, has decreased dramatically, presenting significant challenges for hydrological modeling due to complex interactions between surface and groundwater. In this study, the impact of agricultural activities on streamflow within one of the largest sub-basins of Urmia Lake is assessed using the Soil and Water Assessment Tool (SWAT) for hydrological assessments. To have accurate assessments, land use change detections were considered by a novel method, which merges the Normalized Difference Vegetation Index (NDVI) with the Digital Elevation Model (DEM) to create a two-band NDVI-DEM image, effectively differentiating between agricultural and rangeland fields. Our findings reveal that agricultural development and irrigation, escalating between 1977 and 2015, resulted in increased annual evapotranspiration (ET) (ranging from 295 mm to 308 mm) and a decrease in yearly streamflow, from 317 million cubic meters to 300 million cubic meters. Overall, our study highlights the significant role that agricultural development and irrigation may play in contributing to the shrinking of Lake Urmia, underscoring the need for improved regional water management strategies to address these challenges, though further analysis across additional basins would be necessary for broader conclusions.

The impact of climate change on respiratory health: current understanding and knowledge gaps.

To present an overview of the impact of climate change upon human respiratory health. Climate change is directly impacting air quality. Particulate matter clearly increases mortality rates. Ozone, a longstanding suspect in climate-related injury, turns out not to have the major impact that had been projected at current levels of exposure. The key factors in global warming have been clearly identified, but while these factors collectively cause deleterious changes, a close look at the literature shows that it is unclear to what extent each factor individually is a driver of a specific process. This article summarizes some of those studies. A better understanding of which components of climate change most impact human health is needed in order to re-define environmental standards. PM2.5 needs to be broken down by chemical composition to study the differential impacts of different sources of PM2.5. The detection and study of climate-related changes in respiratory infectious diseases is in a state of relative infancy.

Investigating Interaction Dynamics of an Enantioselective Peptide‐Catalyzed Acylation Reaction

Modern nuclear magnetic resonance (NMR) methods like carbon relaxation dispersion in the rotating frame (13C‐R1ρ) and proton chemical exchange saturation transfer (1H‐CEST) are key methods to investigate molecular recognition in biomacromolecules and to detect molecular motions on the µs to s timescale, revealing transient conformational states. Changes in kinetics can be linked to binding, folding, or catalytic events. Here, we investigated whether these methods allow detection of changes in the dynamics of a small, highly selective peptide catalyst during recognition of its enantiomeric substrates. The flexible tetrapeptide Boc‐l‐(π‐Me)‐His‐AGly‐l‐Cha‐l‐Phe‐OMe, used for the monoacetylation of cycloalkane‐diols, is probed at natural abundance using 13C‐R1ρ and 1H‐CEST. Indeed, we detected differences in dynamics of the peptide upon interaction with the diol. Importantly, these differ depending on the enantiomer of the substrate used. These enantiospecific influences of the substrates on the dynamics of the peptide are rationalized using computational techniques. We find that even though one enantiomer reacts faster, as confirmed by reaction monitoring, the other is more tightly bound in DCM (as confirmed by 1H‐saturation transfer difference (STD) measurements). These findings provide insights into the recognition of the substrates and explain the selectivity differences observed between the solvents toluene and DCM.

Fiducial Reference Measurements for Air Quality Monitoring Using Ground-Based MAX-DOAS Instruments (FRM4DOAS)

The UV–Visible Working Group of the Network for the Detection of Atmospheric Composition Changes (NDACC) focuses on the monitoring of air-quality-related stratospheric and tropospheric trace gases in support of trend analysis, satellite validation and model studies. Tropospheric measurements are based on MAX-DOAS-type instruments that progressively emerged in the years 2010 onward. In the interest of improving the overall consistency of the NDACC MAX-DOAS network and facilitating its further extension to the benefit of satellite validation, the ESA initiated, in late 2016, the FRM4DOAS project, which aimed to set up the first centralised data processing system for MAX-DOAS-type instruments. Developed by a consortium of European scientists with proven expertise in measurements, data extraction algorithms and software design specialities, the system has now reached pre-operational status and has demonstrated its ability to deliver a set of quality-controlled atmospheric composition data products with a latency of one day. The processing system has been designed using a highly modular approach, making it easy to integrate new tools or processing updates. It incorporates advanced algorithms selected by community consensus for the retrieval of total ozone, lower tropospheric and stratospheric NO2 vertical profiles and formaldehyde profiles. The ozone and NO2 products are currently generated from a total of 22 stations and delivered daily to the NDACC rapid delivery (RD) repository, with an additional mirroring to the ESA Validation Data Centre (EVDC). Although it is still operated in a pre-operational/demonstrational mode, FRM4DOAS was already used for several validation and science studies, and it was also deployed in support of field campaigns for the validation of the TROPOMI and GEMS satellite missions. It recently went through a CEOS-FRM self-assessment process aiming at assessing the level of maturity of the service in terms of instrumentation, operations, data sampling, metrology and verification. Based on this evaluation, it falls under class C, which is a good rating but also implies that further improvements are needed to reach full compliance with FRM standards, i.e., class A.

Detection of changes in water salinity based on patrol monitoring and remote sensing methods

A significant number of rivers heavily influenced by anthropogenic pressures are not monitored (or monitored infrequently). For this reason, there is a need to develop modern methods allowing for the ongoing observation of water quality parameters, of which salinity is a key one. As a result of patrol monitoring information on changes in conductivity in the longitudinal profile of the Oder were obtained. The aim of this study is to correlate these results with Sentinel – 2 satellite imagery (VIS + NIR bands) to verify the hypothesis that remote sensing methods can be used to detect salinity changes in inland flowing waters. For this purpose data acquired with the multi-parameter probe during field expeditions, remote sensing methods and Geographical Information Systems tools (such as inter-bands algebra, pixel value extraction) and statistical methods were used. We concluded that the best spectra for salinity detection is Green as well as Salinity Index (SI) mostly showed statistically significant correlations. Remote sensing can be successfully used to detect changes in the salinity of flowing waters.

Detection and Severity Assessment of Temporomandibular Joint Osteoarthritic Changes in Patients with Temporomandibular Disorders Using CBCT

Detection and Severity Assessment of Temporomandibular Joint Osteoarthritic Changes in Patients with Temporomandibular Disorders Using CBCT

Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models

Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer’s disease (AD), allowing non-invasive detection of amyloid-β plaques in the brain. However, the low spatial resolution of PET scans limits the accurate quantification of amyloid deposition due to partial volume effects (PVE). In this study, we propose a novel approach to addressing PVE using a latent diffusion model for resolution recovery (LDM-RR) of PET imaging. We leverage a synthetic data generation pipeline to create high-resolution PET digital phantoms for model training. The proposed LDM-RR model incorporates a weighted combination of L1, L2, and MS-SSIM losses at both noise and image scales to enhance MRI-guided reconstruction. We evaluated the model’s performance in improving statistical power for detecting longitudinal changes and enhancing agreement between amyloid PET measurements from different tracers. The results demonstrate that the LDM-RR approach significantly improves PET quantification accuracy, reduces inter-tracer variability, and enhances the detection of subtle changes in amyloid deposition over time. We show that deep learning has the potential to improve PET quantification in AD, effectively contributing to the early detection and monitoring of disease progression.

A new indanone based dyes bearing dicyanomethylene and salicylidene moieties for selective detection cyanide anion and aromatic amines

Novel chemosensors that are indan-1-one dyes bearing salicylidene moiety were synthesized with good yields and characterized by FT-IR, 1H NMR, 13C NMR, and HRMS methods. Moreover, the molecular structure of 7 was characterization by X-ray and Environmental scanning electron microscopy (ESEM) analyses. The chemosensor potential of dyes were examined in a variety of basic conditions that include various anions, organic amines, and pH ranges by absorption and fluorescence spectra. The naked-eye detection of instant color changes was also observed. The deprotonation mechanism between dyes and anions was investigated by reversibility and 1H NMR studies. pKa values were determined in buffer solution with pH 6–11. Furthermore, organic amines were investigated and found they have two different interaction mechanism, deprotonation and addition. Density Functional Theory (DFT) were used for supporting the experimental results. In addition, the second-order nonlinear optical property of dyes were investigated and showed moderate μβ values.