Smartphone-based digital phenotyping has emerged as a promising approach for monitoring mental health using passive behavioral data. Prior studies have linked smartphone-derived features to depression and anxiety severity; however, knowledge regarding whether short-term changes in symptoms can be captured using passive smartphone data in general population samples remains limited, as does the understanding of how such findings should be interpreted vis-à-vis behavioral patterns and demographic variability. This study aimed to model short-term changes in depression and anxiety severity using passive smartphone data, examine model performance across demographic subgroups, and identify behavioral patterns associated with symptom changes. We collected 2 weeks of smartphone usage data from 95 adults in the general population and assessed depressive and anxiety symptoms using the clinician-rated Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale, respectively. Behavioral features-including physical activity, app use, and screen usage metrics-were extracted and compressed using an autoencoder and principal component analysis. The resulting features-along with age, sex, and baseline Hamilton scores-were used to train random forest classifiers predicting symptom score changes (increase, decrease, or unchanged). Additionally, we examined whether model performance differed across demographic subgroups and whether models excluding baseline scores retained predictive performance, as baseline severity was expected to be a strong predictor. To add explanatory value beyond prediction, behavioral subtypes associated with symptom changes were identified by applying unsupervised clustering. The model exhibited moderate performance in predicting changes in the Hamilton Depression Rating Scale (mean accuracy=0.70, mean area under the receiver operating characteristic curve=0.74) and Hamilton Anxiety Rating Scale (mean accuracy=0.65, mean area under the receiver operating characteristic curve=0.69) scores. Performance varied according to demographics, with reduced accuracy among younger adults and females, although these differences were not significant in permutation tests. Excluding baseline Hamilton scores diminished performance substantially, suggesting that baseline symptom severity accounted for a substantial proportion of the predictive performance. Clustering revealed 4 distinct behavioral subtypes according to smartphone usage patterns. A cluster characterized by structured, daytime-focused smartphone use and lower temporal entropy demonstrated greater improvement in depressive symptoms, whereas clusters with lower and irregular usage patterns exhibited minimal improvement or worsening. Passive smartphone-derived behavioral data demonstrated moderate ability to model short-term symptom changes in this predominantly nonclinical sample. However, a substantial proportion of the predictive performance was attributable to baseline symptom severity, underscoring that passive smartphone data may provide modest supplementary information rather than robust stand-alone predictive value. Nevertheless, clustering analyses indicated that passive data may still assist in identifying behaviorally distinct subtypes associated with different depressive symptom trajectories. These findings reflect a practical contribution to digital phenotyping research by elucidating both the potential and constraints of passive smartphone data for short-term symptom monitoring in small general population samples.
To evaluate the relationship between smartphone addiction and thumb-wrist symptoms and first dorsal compartment ultrasonographic abnormalities in young adults, and to investigate whether addiction severity and usage patterns are associated with these findings. This multcentre prospective observational study included 477 university students aged 18-30 years. Smartphone addiction was assessed using the Smartphone Addiction Scale-Short Version (SAS-SV). Clinical evaluation included bilateral Finkelstein testing and pain assessment using the Visual Analog Scale (VAS). Standardized ultrasonographic examinations of the first dorsal extensor compartment were performed according to OMERACT recommendations. Tendon sheath thickening, fluid accumulation, retinacular thickening, and Doppler activity were recorded as positive findings. Participants classified as addicted at baseline were reassessed at six months and re-stratified according to their follow-up addiction status. Smartphone addiction was significantly associated with female sex, symptom presence, higher pain scores, longer daily smartphone use, and increased ultrasonographic abnormalities on both dominant and non-dominant sides (all p < 0.001). Addicted participants demonstrated higher VAS scores and greater prevalence of bilateral ultrasonographic findings compared with non-addicted individuals. Dominant-side ultrasonographic abnormalities were associated with addiction status, symptom presence, and daily usage duration, whereas non-dominant-side findings were additionally associated with grip pattern. Among baseline-addicted participants reassessed at six months, those who no longer met the addiction threshold showed significant reductions in pain scores and regression of ultrasonographic findings, whereas those who remained addicted demonstrated persistence or progression of symptoms and structural findings despite partial reductions in screen time. Smartphone addiction is associated with increased thumb-wrist symptoms and first dorsal compartment ultrasonographic abnormalities in young adults. These findings suggest that excessive smartphone use may represent a relevant biomechanical exposure contributing to early tendon changes. Behavioral modification may facilitate partial recovery, whereas persistent abnormalities in addicted individuals highlight the importance of addressing usage behavior in prevention and management strategies. Further longitudinal studies with objective exposure measurements are warranted.
Professional UAV thermal imaging systems are widely used for inspection, environmental monitoring, search and rescue, agriculture, and technical diagnostics. However, their cost limits their use in education, preliminary field screening, rapid prototyping, and low-resource applications. This study evaluates a minimum-cost indirect UAV thermal sensing workflow based on a DJI Mini 4K consumer drone, a lightweight Servo King9000 smartphone, and a UTi260M smartphone-connected infrared thermal camera. In the proposed configuration, the smartphone displayed and recorded the thermal stream, while the onboard RGB camera of the UAV recorded the smartphone-displayed infrared video during flight. The aim was not to develop a radiometric UAV thermal imaging platform, but to determine whether such a low-cost configuration can provide qualitative presence/absence indication of clear thermal hotspots and to identify its operational limits. The system was experimentally assessed under no-payload and payload conditions, daylight and nighttime illumination, and several low-altitude operating heights. Additional motor-region thermal observations were performed using a UTi260T handheld thermal camera under loaded and unloaded operating conditions. The complete UAV-payload configuration had a measured mass of approximately 340 g, corresponding to an effective added payload of 91 g and a payload-to-UAV mass ratio of 36.5%. Payload operation reduced near-ground flight endurance from approximately 25 min to 14 min 40 s. The maximum observed motor-region temperature increased from 24.9 °C under unloaded operation to 42.0 °C under loaded operation, while motor thermal asymmetry increased from 4.8 °C to 7.6 °C. Nighttime and low-glare operation improved the readability of the smartphone-displayed thermal stream, with the most practical usability observed at approximately 10-20 m. The results show that the proposed workflow is feasible only for short-range qualitative thermal screening and clear hotspot presence/absence indication. The UAV-recorded video should not be interpreted as direct thermal data, but as an RGB recording of a smartphone display showing thermal information. Therefore, the workflow is not suitable for quantitative temperature measurement, radiometric thermal mapping, or accurate thermal shape delineation. The main operational limits are payload mass, suspended-load oscillation, display readability, reduced endurance, motor-region thermal loading, sensitivity to payload alignment, and the absence of raw radiometric data. Direct UTi260M smartphone-recorded thermal frames were additionally used for pixel-size-assisted qualitative verification of practical reference thermal targets, including a human-sized target and a vehicle-sized target, at selected low-altitude operating heights.
This cross-sectional study examined the association between smartphone dependence and sport participation among Chinese adolescents and assessed whether self-control, health beliefs, and interpersonal support were statistically related to this association. A total of 1,610 students in Grades 5-9 completed self-report measures of smartphone dependence, sport participation, self-control, health beliefs, and interpersonal support. Structural equation modeling with bootstrapping was used to test a theoretically specified chain mediation model involving self-control and health beliefs. A separate simple moderation analysis using PROCESS Model 1 examined whether interpersonal support moderated the direct association between smartphone dependence and sport participation. Smartphone dependence was negatively associated with sport participation. Bootstrapped structural equation modeling indicated three significant cross-sectional indirect associations: through self-control, through health beliefs, and through the theoretically specified sequence of self-control and health beliefs. In the separate moderation analysis, interpersonal support moderated the direct association between smartphone dependence and sport participation. The negative association was more pronounced among adolescents reporting higher interpersonal support, suggesting that general perceived support may not necessarily function as sport-specific support. The findings identify self-regulatory, health-cognitive, and social-contextual correlates of sport participation in the context of smartphone dependence. Given the cross-sectional design, the results should be interpreted as statistical associations rather than causal or temporal processes. They may inform future longitudinal and intervention studies examining whether self-regulatory skills, sport-related health beliefs, and action-oriented support can promote adolescent sport participation.
Academic burnout is a prevalent issue among college students, yet research on its relationship with physical exercise from a person-centered perspective remains limited. This study aimed to identify latent profiles of physical exercise among college students and to examine the mediating roles of self-control and problematic smartphone use in the association between these exercise profiles and academic burnout. A questionnaire survey was conducted among 722 Chinese college students. Latent profile analysis and bootstrap mediation analysis were employed to analyze the data. The results revealed three distinct exercise profiles: occasional exercisers (27.9%), developing exercisers (31.7%), and regular exercisers (40.3%). Significant differences were found across the three profiles in self-control, problematic smartphone use, and academic burnout. Specifically, regular exercisers reported the highest self-control and the lowest levels of problematic smartphone use and academic burnout, followed by developing exercisers, with occasional exercisers showing the least favorable outcomes. Mediation analyses indicated that self-control significantly mediated the relationship between both the developing and regular exercise profiles (compared to occasional exercisers) and academic burnout. Moreover, self-control and problematic smartphone use acted as sequential mediators in these relationships. However, problematic smartphone use alone did not show a significant mediating effect. These findings highlight the heterogeneity in college students' physical exercise patterns and suggest that interventions aimed at reducing academic burnout should consider promoting regular physical exercise, which appears to be associated with higher self-control and subsequently lower problematic smartphone use. Tailored strategies targeting different exercise profiles may be more effective in addressing academic burnout.
This study aimed to investigate the sex-specific differences in smartphone addiction, physical activity levels, and cognitive functions among university students. A cross-sectional survey was conducted with 256 university students aged 18-25. Participants completed questionnaires using the Smartphone Addiction Scale Short Version to assess their level of smartphone addiction. The working memory and selective attention domains of cognitive function were evaluated, and the International Physical Activity Questionnaire was used to determine participants' self-reported physical activity levels. The results revealed significant sex differences, with male students exhibiting higher levels of smartphone addiction (male = 46.42 ± 9.37; female = 38.27 ± 7.63) and greater physical activity (male = 3752 ± 1876; female = 3447 ± 1748) than their female counterparts. Additionally, female students demonstrated superior performance on selective attention tasks, including reaction time (female = 463.00 ± 50.53; male = 457.34 ± 59.31) and accuracy (female = 92.26 ± 6.53; male = 89.60 ± 8.39) across varied conditions, whereas no significant sex differences were observed in working memory or overall reaction time. These findings suggest that sex-specific factors may influence differences between male and female participants in smartphone use, cognitive function, and physical activity.
This study aimed to determine whether flagship smartphones can approach the performance of professional digital single-lens reflex (DSLR) cameras using a standardized workflow incorporating color calibration and optical zoom. Three DSLR cameras (Canon EOS 5D Mark IV, Canon EOS 80D, Nikon D610) and two smartphones (iPhone 17 Pro Max, Galaxy S24 Ultra) were used to capture nine standardized extraoral and intraoral views for each of 25 volunteers. Images were evaluated for color accuracy, dimensional accuracy, and image quality. Statistical analyses were conducted using one-way repeated-measures analysis of variance and paired t-tests, with Bonferroni correction applied for multiple comparisons (α = 0.05). Gray-card calibration significantly reduced smartphone image ΔE values (P < 0.001), resulting in lower ΔE values than those of the DSLR group with standardized white balance (P < 0.001). Regarding dimensional accuracy, images captured with the iPhone 17 Pro Max at 4× optical zoom showed no significant difference from DSLR cameras (P = 0.178), whereas the Samsung device significantly underestimated arch width (P = 0.041). Samsung achieved the most favorable BRISQUE score. Under a standardized workflow incorporating color calibration and appropriate optical zoom, smartphone photography achieved gray-card-based color accuracy and relative dimensional consistency comparable to those of DSLR cameras, providing a more convenient and feasible imaging option. However, DSLR cameras still demonstrated advantages in clinically demanding aesthetic cases. Using a standardized workflow that includes appropriate optical zoom, professional dental lighting, and gray-card-based color calibration, smartphone photography can achieve relatively satisfactory reproduction of dental color and dimensional consistency, representing a potentially reliable and cost-effective option for clinical documentation.
Smartphone-based electrocardiography (ECG) is increasingly explored as a practical tool for cardiac assessment in veterinary species. The aim of this prospective observational study was to investigate the usability and reliability of a new smartphone-based ECG system (Eko DUO) in healthy foals. A total of 39 foals, aged 1 to 59 days, were enrolled. ECGs were recorded using the smartphone device in all animals, while a subgroup of 10 foals also underwent simultaneous recording with a conventional base-apex ECG (rECG) to enable direct comparison. All tracings were reviewed blindly by a single evaluator to assess interpretability, rhythm, and ECG parameters. Statistical analysis was performed. The smartphone ECG (sECG) provided readable tracings in all foals without requiring clipping or sedation. Sinus rhythm was identified in every subject, and heart rate classification (normal, bradycardia, or tachycardia) showed complete concordance with the rECG, supporting its clinical applicability for rapid cardiac assessment. No significant discrepancies were detected for heart rate, PR interval, or QT interval measurements, although P wave duration was shorter and QRS complex slightly longer in sECG recordings. The polarity of QRS complexes matched across systems, while P wave polarity varied. A moderate agreement concerning ECG quality and artifact was found. The smartphone ECG system showed good performance in field conditions, enabling rapid acquisition of clinically interpretable tracings and accurate heart rate evaluation, though variations in wave morphology should be acknowledged during clinical use.
Equine sports medicine is increasingly oriented toward objective, field-based monitoring systems that support both performance optimization and welfare assessment. In this context, smartphone-based technologies have emerged as accessible tools capable of integrating data acquisition, processing, and interpretation within a single platform. This narrative review aims to examine the role of smartphones in equine sports medicine, focusing on their function as standalone sensing devices and as gateways for wearable and external sensor systems. The analysis is based on a structured synthesis of current literature addressing technological foundations, including embedded sensors, connectivity architectures, and artificial intelligence-driven data processing, as well as their clinical applications across locomotor, cardiovascular, respiratory, behavioural, and thermoregulatory domains. Evidence indicates that smartphone-based systems improve the feasibility of longitudinal monitoring and facilitate real-time decision-making in field conditions, while enhancing communication between veterinarians, trainers, and owners. However, their performance remains influenced by acquisition conditions, system variability, and algorithmic constraints, requiring careful validation and contextual interpretation. In addition, challenges related to data governance, privacy, and ethical use remain insufficiently addressed. Overall, smartphone-based technologies represent enabling tools that support a transition toward more integrated, data-driven, and welfare-oriented management of the equine athlete, while highlighting the need for standardisation and regulatory development.
Background/Objectives: Keratoconus (KC) is a chronic disease that causes progressive corneal thinning and steepening, thereby negatively impacting visual acuity. Although corneal topography and keratometry are the primary measures to diagnose KC, access to these methods can be limited by various factors. To address these limitations, this study evaluates a novel low-cost deep-learning algorithm that infers keratometric categories from smartphone-assisted Placido ring photographs. Methods: Development utilized 1240 healthy control eye images and 188 K1-labeled KC images for pretraining, without using their K1 labels. A Variational Autoencoder with KL divergence regularization (AutoEncoderKL) was trained on this pool; its encoder generated latent features for KC images (n = 535). A held-out set (n = 70) with Pentacam keratometry was labeled by K1 into <40 D, 40-47 D, and >47 D. An ensemble classifier chosen via grid search and cross-validation used the encoder features. Performance was assessed for accuracy, precision, recall, and F1-score. Results: The model achieved 91% accuracy across all classes. Precision of the model was 0.77 (<40 D), 0.98 (40-47 D), and 0.86 (>47 D); recall was 0.83, 0.91, and 1.00; and F1-scores were 0.80, 0.94, and 0.92, respectively. Notably, the model achieved perfect recall for the >47 D K1 category. Conclusions: A smartphone-assisted Placido ring imaging approach was able to predict K1-based keratometric categories without requiring tomographic or keratometric measurements as model inputs at inference. These findings provide preliminary proof-of-concept for the potential use of smartphone-assisted Placido ring images as a low-cost approach for K1-based stratification. Larger externally validated studies across different sites, devices, operators, printed Placido discs, acquisition conditions, and patient populations are required before clinical utility can be assessed.
Smartphone global navigation satellite system (GNSS) positioning is degraded by low-cost antennas, limited receiver hardware, multipath propagation, and noisy code pseudorange observations. Existing correction methods often improve stochastic weighting, estimate coordinate-domain corrections, or smooth receiver trajectories, but they rarely estimate how each satellite contributes to the horizontal position error while preserving line-of-sight (LOS) geometry. This study presents a random-forest-assisted geometry-aware correction method that combines satellite-wise LOS projection error estimation with exponential temporal weighted least squares (Temporal WLS). The horizontal error between the smartphone National Marine Electronics Association (NMEA) solution and the F9P reference position is projected onto each satellite LOS direction and used as the learning target. A random forest model is trained using 26 smartphone GNSS features, including geometry, signal strength, code-derived variation, uncertainty, automatic gain control, and state flags. The predicted LOS errors are fused with satellite geometry through epoch-wise WLS and Temporal WLS. In same-session front-70/back-30 validation, the horizontal root mean square (RMS) error decreased from 2.747 m to 1.033 m. Excluding one suspected non-co-located reference session further reduced the RMS error from 2.867 m to 0.362 m.
Intermittent and non-invasive glucose monitoring offers remarkable benefits for personalized diabetes management. We present a novel smartphone-integrated near-infrared (NIR) fluorescent microfluidic sensor for efficient sweat collection and fast, reliable detection of glucose. The microfluidic sensor contains four microchambers, each equipped with a bilayer membrane: the upper is sensing layer consisting of polyurethane matrix and non-enzymatic glucose probe PY-POFs, while the lower is light-converting layer consisting of PMMA matrix and up-conversion nanoparticles (UCNPs). Upon 808-nm NIR excitation, the light-converting layer excited the sensing layer via upconversion luminescence, and the glucose-sensitive fluorescence peaking at 470 nm is captured with a smartphone. The sensor exhibits a rapid response time of 15 s, excellent selectivity and stability due to the non-enzymatic nature, as well as a low detection limit of 20 µM because of low-background interference. Moreover, the practical applicability of our device is verified by tracking glucose fluctuations in sweat samples from human volunteers under pre- and post-prandial conditions, with results well correlated to those acquired by commercial blood glucose meters. The prototype demonstrated holds significant promise as a reliable, portable, and user-friendly sensing platform, suitable for rapid intermittent point-of-care non-invasive metabolic monitoring.
Stroke is a leading cause of global disability in the aging population, with cognitive impairments playing a significant role. Prior research has shown that subjective cognitive concerns (SCCs) can predict later dementia and serve as an essential indicator for poststroke functional rehabilitation. The use of smartphone-based ecological momentary assessment (EMA) in real-world environments may help us understand how SCCs relate to daily functioning in individuals aging with stroke, thereby guiding cognitive rehabilitation and prevention efforts. Our study aimed to use EMA to examine the real-time associations between SCCs and daily activity participation in persons aging with stroke. This longitudinal observational study used smartphone-based EMA for real-time assessment of individual cognitive concerns and participation in various daily activities. EMA survey items, including SCCs (concentration and learning) and participation in daily activities (location, company, current activity, and self-appraisals of performance, help needed, satisfaction, and engagement), were collected 5 times per day for 2 weeks. Multilevel models were used to analyze the data. A total of 202 individuals with mild-to-moderate chronic stroke participated in the study (n=90, 44.6% female; n=89, 44.1% Black; n=182, 90.1% ischemic stroke; mean age 59.7, SD 11.7 years). SCCs were concurrently lower when participants engaged in activities of daily living (ADL; B=-0.04, 95% CI -0.07 to -0.01; P=.02), instrumental ADL (B=-0.05, 95% CI -0.07 to -0.02; P<.001), cognitively stimulating activities (B=-0.05, 95% CI -0.08 to -0.02; P<.001), and social activities (B=-0.05, 95% CI -0.08 to -0.02; P=.002); when participants were located in a friend's home (B=-0.10, 95% CI -0.17 to -0.02; P=.001); and when they spent time with family members (B=-0.07, 95% CI -0.10 to -0.04; P<.001), friends (B=-0.05, 95% CI -0.10 to -0.01; P=.01), and spouse or partners (B=-0.04, 95% CI -0.07 to -0.01; P=.02). Conversely, SCCs were higher when participants were in the hospital (B=0.39, 95% CI 0.25-0.53; P<.001). Additionally, greater SCCs were concurrently associated with worse ratings of performance (B=-0.05, 95% CI -0.06 to -0.05; P<.001), satisfaction (B=-0.05, 95% CI -0.06 to -0.05; P<.001), and activity engagement (B=-0.05, 95% CI -0.06 to -0.04; P<.001). EMA provides an effective means of understanding the links between poststroke cognition and participation in daily activities. Our findings suggest that ADL, instrumental ADL, cognitively demanding activities, and socially engaging activities may lessen cognitive concerns among stroke survivors, implying that clinicians should schedule these activities to help reduce poststroke cognitive issues. Conversely, interventions that enhance cognition may increase participation in these challenging activities. Tracking cognition, everyday activity involvement, and their interactions in real-world settings could ultimately help develop rehabilitation and prevention strategies for individuals at risk of dementia due to stroke.
Recycling reduces environmental pollution, conserves natural resources, and minimizes waste, while monitoring ferric ions is important for evaluating iron-related biological and environmental conditions. The present work addresses two important challenges: recycling plastic waste and developing a simple sensing platform for Fe3+ detection. In this study, post-consumer PET bottles were converted into highly fluorescent carbon quantum dots (PET-CQDs) with an average diameter of 5 ± 2 nm for the detection of ferric ions (Fe3+). The PET-CQDs exhibited Fe3+ detection through an ON-OFF fluorescence sensing platform. A linear range of 10-100 µM was achieved, with a correlation coefficient of R 2 ≥ 0.9886. The detection and quantification limits were 3.66 µM and 11.1 µM, respectively. Additionally, a smartphone-based visual detection method achieved a linear range of 10-70 µM for Fe3+, with a correlation coefficient of R 2 ≥ 0.9877. Using smartphone-assisted detection, the detection and quantification limits were recorded as 4.4 µM and 13.4 µM, respectively. The ON-OFF sensing platform was further examined in a deproteinized human serum as a proof-of-concept matrix study, showing that the PET-CQDs retained Fe3+-responsive fluorescence behavior under the tested conditions. Furthermore, the greenness of the proposed method was assessed using various green metrics, including ComplexMoGAPI, AGREEprep, and BAGI tools.
Accurate and reliable detection of ascorbic acid (AA) is of paramount significance for clinical diagnosis and health surveillance. Consequently, the rational design of advanced functional materials and the exploration of their potential in AA sensing have emerged as a pivotal research frontier. Herein, a heterometallic EuIII-BiIII cluster-encapsulated antimonotungstate (HDMEA)9 Na18H3{[Bi10Eu2(H2O)4(DMEA)2(H2OA)4W10O36][B-α-SbW9O33]6}·124H2O (1, DMEA = N,N-dimethylethanolamine, H2OA = oxalic acid) was successfully synthesized via a multicomponent co-assembly strategy. Notably, 1 exhibits intense fluorescence emission in aqueous media and functions as a highly sensitive fluorescent sensor for AA, achieving a low detection limit of 0.038 μM in a linear range of 40-200 μM. Intriguingly, the aqueous solution of 1 undergoes a distinct color transition from colorless to orange upon the addition of AA. Capitalizing on this chromogenic response, a portable sensing platform was further constructed, which transduces visual color signals into digital readouts through a smartphone-based colorimetric application, realizing quantitative detection of AA with an LOD of 0.45 μM in the linear range of 0-400 μM. Both the 1-based fluorescent sensor and the smartphone-assisted colorimetric assay were successfully validated for the detection of AA in real samples. This work elucidates the unique synergistic interactions between Eu3+ and Bi3+ ions in the fabrication of multicomponent polyoxometalates and highlights the immense potential of polyoxometalate-based materials for applications in environmental monitoring and biomedical sensing.
A new cholic acid-salicylaldehyde conjugate, probe CASA, was facilely constructed through one-step esterification. The probe sensitively and selectively detected both N2H4 and pH, with the salicylaldehyde group as the sensing site. Based on the combined AIE/ESIPT mechanism, the probe exhibited a yellow-green turn-on fluorescent response to N2H4 with a low detection limit of 26 nM and a rapid detection time of 10 min. Through the ICT process, the probe displayed a light blue turn-on fluorescence signal for neutral pH variations in a linear range of 5.0-7.8 with a pKa value of 6.26. The microstructural attributes of the probe during assembly could elucidate the recognition processes. Probe CASA enabled quantitative analysis of N2H4 and pH in real environmental samples. A smartphone-assisted sensing system and probe-coated portable test strips were developed to facilitate convenient and on-site testing. Additionally, the probe possessed excellent biocompatibility and low cytotoxicity, making it possible to monitor the changes of N2H4 and pH within living cells. This dual-channel N2H4-pH independent detection can prevent spectral crosstalk, facilitate instrument simplification, and provide a more intelligent and reliable analytical toolkit for complex systems. The findings are anticipated to provide more innovative strategies of highly efficient fluorescent probes for multi-analyte detection.
A ratiometric fluorescence platform for sensing arsenate with high-performance has been developed with Cu2+-functionalized Zr metal organic framework (Cu@Zr-MOF) fluorescent nanozyme. The doping of Cu2+ provides the catalytic active site to decompose O2 into •OH and 1O2, which oxidizes non-fluorescent substrate o-phenylenediamine (OPD) into product (oxOPD) with yellow fluorescence. When As(V) is present, the intrinsic blue fluorescence of Zr-MOF enhances remarkably by weakening the ligand-to-metal charge transfer (LMCT) accompanied by the fluorescence decrease of oxOPD owing to inner filter effect (IFE). Furthermore, the solutions present a distinguishable color tonality from yellow to blue, which is converted to digital value by smartphone, achieving visual detection of As(V) with a low LOD of 0.67 μM. The specific recognition of Zr-O clusters toward As(V) enables this sensor high selectivity. Compared to other MOF-based ratiometric fluorescence assays for As(V), this method is portable and cost-effective owing to the direct coordination of Cu@Zr-MOF nanozyme instead of fragile and high-cost natural enzyme with As(V). This work not only develops a portable method for sensing As(V) but also expands the potential application of functionalized MOF in on-site monitoring.
Rapid and decentralized glucose monitoring requires analytical platforms that combine sensitivity, portability, affordability, and environmental sustainability. In this work, iron- and nitrogen-co-doped carbon dots (Fe,N-CDs) were synthesized through a simple one-step thermal approach and employed as nanozymes exhibiting intrinsic peroxidase-like activity for colorimetric sensing. Structural and morphological characterization confirmed the successful formation of Fe,N-CDs with favorable catalytic properties. The synthesized nanozymes efficiently catalyzed the oxidation of o-phenylenediamine in the presence of hydrogen peroxide producing a concentration-dependent colorimetric response with a linear range of 50-600 µM and a detection limit of 20 µM. By coupling the nanozyme system with glucose oxidase, a cascade sensing strategy was established for indirect glucose determination over a linear range of 100-600 µM. To enable portable and point-of-care analysis, the sensing platform was integrated with smartphone-assisted image analysis and an Arduino-based RGB detection module providing dual-mode digital colorimetric readout. Both portable approaches showed excellent agreement with conventional spectrophotometric measurements confirming the reliability of the proposed analytical platform. The developed assay demonstrated high selectivity toward glucose over common interfering species, excellent reproducibility (RSD < 1.5%), and successful application to serum glucose analysis. Furthermore, sustainability assessment using the whiteness score, BAGI (Blue Applicability Grade Index), RAPI (Red Analytical Performance Index), and VIGI (Violet Innovation Grade Index) tools confirmed favorable environmental compatibility, analytical performance, applicability, and innovation. Overall, the proposed platform combines low-cost nanozyme synthesis, dual-mode portable detection, reliable analytical performance, and sustainability offering a promising strategy for point-of-care glucose monitoring and decentralized biosensing applications.
Vitamin B2 (VB2) and Vitamin B6 (VB6) are essential micronutrients for maintaining normal physiological functions. Deficiency and excessive intake of vitamins can lead to significant health issues. Herein, a single-component cerium-based MOF sensor (Ce-MOF), {[Ce2(BTB)2(DMF)2]•2DMF•2.5H2O}n is synthesized via solvothermal methods. The as-prepared Ce-MOF exhibits excellent water stability and distinct ligand-centered emission, which serves as an efficient dual-target sensor, enabling highly selective and sensitive ratiometric and colorimetric detection of VB2, as well as fluorometric detection of VB6. Upon addition of VB2, Ce-MOF exhibits a new emission peak at 527 nm, accompanied by a visible fluorescence color change from blue to yellow-green. The quenching constant is 8.41 × 104 M-1, and the detection limit is as low as 0.052 μM. In the case of VB6, the emission peak gradually red-shifts to 395 nm, with a quenching constant of 2.06 × 104 M-1 and a detection limit of 0.355 μM. Moreover, a Ce-MOF-embedded portable hydrogel combined with smartphone-assisted RGB analysis is fabricated, enabling visual and quantitative detection of VB2 and achieving excellent recovery (92.4-106.6%) in vitamin tablets, banana extracts, and sugar. These results highlight the potential of Ce-MOF as an efficient and practical dual-target sensor for nutritional analysis.
This research represents an advancement in smartphone-based image acquisition methodology, building upon a previous study to estimate the essential oil content of bergamot fruits in situ using a deep learning approach. To overcome an operational constraint due to a bulky portable dark box to standardise illumination, this study proposes a more versatile solution: a mobile application based on a colour card reference. By replacing physical shielding with digital compensation, the app functions as a local colourimetric sensor, enabling real-time correction of images acquired directly in the orchard, regardless of environmental variables such as direct sunlight or shadows. Workflow relies on an automated calibration procedure. Upon image acquisition, the application utilises ArUco Markers to autonomously detect and extract both the colour card and the fruit surface. The core of the innovation lies in the colour calibration algorithm based on RGB histogram matching logic, which calculates the precise chromatic transformation required to align the field data with the reference card data (acquired under controlled conditions). These calculated parameters are then dynamically mapped onto the fruit's image. The final output is a normalised high-fidelity image, ready for the calculation of chromatic indices, such as the citrus colour index, or for seamless integration into predictive models. The results show that the application is a valid tool for colour calibration, thanks to the good agreement with the values obtained using the inspection chamber. The latter can therefore be replaced by the app, which allows reliable results to be obtained even when used on its own.