Detection Applications Research Articles

CsPbIBr2 Thick Film With (100)‐Preferred Orientation Enables Highly Sensitive X‐Ray Detector by a Spray‐Coating Method via Dual‐Additive‐Assisted Solution Strategy under Atmospheric Environment

AbstractThe realization of large‐area high‐quality perovskite thick films under an atmospheric environment without humidity control is confronting an urgent challenge for the applications in X‐ray detection and imaging. A spray‐coating method with air as the gas carrier is employed to achieve large‐area perovskite thick films for X‐ray flat‐panel detection applications. To further improve the quality of the thick films, the 10 µm‐thick CsPbIBr2 films with (100)‐preferred orientation, low trap density (7.1 × 1012 cm−3) and high resistivity corresponding to low dark current (3.2 × 10−9 A) are prepared via dual‐additive‐assisted (NH4SCN and L‐α‐phosphatidylcholine (LP)) solution strategy under atmospheric environment with relative humidity of 60% (60% RH). Thus, the metal‐semiconductor‐metal‐structured (Au/CsPbIBr2/Au) transverse X‐ray detection device under 40 KeV X‐ray irradiation possesses a high sensitivity up to 1420 µC Gyair−1 cm−2 under a weak electric field of 18 V mm−1 and a low dose rate of 3.3 µGyair s−1 that is lower than a dose rate of ≈5.5 µGyair s−1 required for routine medical diagnosis. Strikingly, the unencapsulated device maintains 80% (1120 µC Gyair−1 cm−2) of the original sensitivity after 1000 h under the atmospheric environment with 45–55% RH. These results pave the way for the practical applications of perovskite thick films in X‐ray medical detection and imaging.

Binding-driven forward tearing protospacer activated CRISPR-Cas12a system and applications for microRNA detection

CRISPR-Cas12a system, characterized by its precise sequence recognition and cleavage activity, has emerged as a powerful and programmable tool for molecular diagnostics. However, current CRISPR-Cas12a-based nucleic acid detection methods, particularly microRNA (miRNA) detection, necessitate additional bio-engineering strategies to exert control over Cas12a activity. Herein, we propose an engineered target-responsive hairpin DNA activator (TRHDA) to mediate forward tearing protospacer activated CRISPR-Cas12a system, which enables direct miRNA detection with high specificity and sensitivity. Target miRNA specifically binding to hairpin DNA can drive forward tearing protospacer in the stem sequence of hairpin structure, facilitating the complementarity between crRNA spacer and protospacer to activate Cas12a. Upon the hairpin DNA as input-responsive activator of Cas12a, a universal biosensing method enables the multiple miRNAs (miR-21, let-7a, miR-30a) detection and also has exceptional capability in identifying single-base mismatches and distinguishing homologous let-7/miR-30 family members. Besides, TRHDA-mediated Cas12a-powered biosensing has realized the evaluation of miR-21 expression levels in diverse cellular contexts by intracellular imaging. Considering the easy programmability of hairpin DNA in responsive region, this strategy could expand for the other target molecules detection (e.g., proteins, micromolecules, peptides, exosomes), which offers significant implications for biomarkers diagnostics utilizing the CRISPR-Cas12a system toolbox.Graphical abstract

Innovative electrochemical sensor based on platinum group metal complexes for determination of nitrite: Application for simultaneous detection of nitrite and tartrazine

In the context of this experimental study, new palladium and ruthenium complexes based on a symmetric ligand, named 2E-Peaemp ((3E,3’E)-3,3’-(((piperazine-1,4-diyl bis (ethane-2,1-diyl)) bis (azanediyl)) bis (ethan1-yl-1-ylidene)) bis (6-methyl-2H-pyran-2,4(3 H)-dione), and their hybrid compounds with carbon nanotubes were prepared and characterized. The sensors prepared using a cavity microelectrode (MEC) modified with a mixture of graphite (CG), carbon nanotubes (MWCNT) and the synthesized complexes (Pd-2E-Peaemp and Ru-2E-Peaemp) were applied to detect the pollutants such as Nitrites and Tartrazine separately and simultaneously in aqueous solutions and real samples (Tap water, wastewater and bottled commercial spring water). The detection limits (LODs) determined for nitrites and tartrazine are obtained using MEC/CG-MWCNT-Pd-2E-Peaemp electrode, and the electrochemical measurements demonstrated a sensitive and rapid response in the analysis of these species. LODs are 0.39 µM and 0.11 µM, respectively, within the concentration range of 0–909 µM for nitrites and 0–97 µM for tartrazine. This electrode modified by the palladium complex is also used to simultaneously detect nitrite and tartrazine analytes using the differential pulse voltammetry (DPV) technique, indicating that the proposed sensor can be an interesting alternative tool to measure these species in real samples. The application of this electrode in these natural environments reveals that the developed sensor can detect low concentrations of nitrite (NO2-; trace) without any interference from other ions substances as acetate (CH3COO-), borate (BO3−3), phosphate (PO4−3), dihydrogen ( 2 H+), sulfate (SO4−2), sodium (Na+), hydroxide (OH-), potasium (K+), chloride (Cl-), magnesium (I) ( Mg+), nitrate (NO3-), calcium (Ca2+), carbonate (CO3−2).

The development of carbon nanostructured biosensors for glucose detection to enhance healthcare services: a review

In this review, the forefront of biosensor development has been marked by a profound exploration of carbon nanostructured materials for the specific application of glucose detection. Moreover, this progressive line of inquiry capitalizes on the distinctive attributes of carbon nanostructured materials such as carbon nanotubes, carbon quantum dots, and graphene which exhibit unique characteristics in the development of biosensor engineering design. It also enhanced analytical performances regarding the limit of detection, selectivity, sensitivity, and reproducibility towards glucose detection in biological samples. Most importantly, the strategic integration of carbon nanostructured-based biosensor architectures has played a significant role in advancements, characterized by heightened sensitivity, exquisite selectivity, and augmented stability in glucose detection processes. Furthermore, utilizing these advanced materials has engendered a transformative impact on electrochemical properties, propelling the biosensors to achieve rapid and precise glucose-sensing capabilities. The confluence of carbon nanostructures with biosensor technology has not only elevated the scientific understanding of glucose detection mechanisms. Still, it has also paved the way for miniaturized and portable biosensors. This transformative shift holds great promise for the realization of point-of-care diagnostics, representing a pivotal step towards durability and efficient glucose monitoring in health/medical care. These advancements emphasize the crucial role of carbon nanostructured-based biosensors in opening the way to a new avenue of superiority and effectiveness in diabetes management. Conclusively, the challenges and, in a forward-looking stance, the prospective futures of glucose biosensors anchored on carbon nanostructured frameworks were considered.

Biomimetic hair-assisted GaN optical devices for bidirectional airflow detection

Airflow sensing plays a pivotal role in numerous fields, including medicine, industry, and environmental monitoring. However, detecting bidirectional airflow using a single sensing unit poses significant challenges. In this work, a miniature airflow sensing device is introduced, utilizing a GaN optical chip integrated with a biomimetic hair structure. The sensing device comprises a monolithic GaN chip that handles both light emission and detection. The biomimetic hairs, constructed from nylon fibers and PDMS film, undergo structural bending in converting airflow signals into optical changes, modulating the light captured by the on-chip detector. The intensity of the airflow directly correlates with the bending extent of the biomimetic hair, facilitating the precise detection of airflow rates through changes in the photocurrent. The integrated device can measure a wide range of airflow rates from −23.87 ms−1 to 21.29 ms−1, and exhibit a rapid response time of 13 ms and a detection limit of 0.1 ms−1. Characterized by its compact size, fast response time, and bidirectional detection ability, the developed device holds immense potential for applications in breath detection, speech recognition, encoding information, and the realization of logic operations.

High‐Resolution X‐Ray Imaging With 0D Organic–Metal Halide Scintillator Featuring Reversed Exciton Trapping

AbstractScintillators, essential for applications in nuclear medicine, radiation detection, and industrial inspection, convert high‐energy radiation into visible light. Manganese (Mn)‐based inorganic–organic hybrid materials are distinguished by their thermal stability, mechanical strength, and flexibility. However, the effects of temperature on Mn(II)‐based hybrid scintillators have not been clearly analyzed, making the elucidation of their temperature‐dependent luminescence mechanisms particularly important. A notable advancement is the synthesis of Mn‐1 nanocrystals (NCs) using methyltriphenylphosphonium chloride (mtppCl) and MnCl₂. These NCs exhibit distinctive temperature‐dependent photoluminescence luminescence: the intensity decreases from 77 to 150 K but paradoxically increases at higher temperatures due to anomalous thermal exciton behavior in the [MnCl₄]2⁻ tetrahedra. Besides, Mn‐1 NCs achieve a detection limit of 1.01 µGyair/s, surpassing medical diagnostic standards and outperforming commercial scintillators such as Bi₄Ge₃O₁₂ (BGO). Additionally, they show exceptional stability under continuous irradiation and can be incorporated into a flexible scintillating film with a resolution of 11.3 lp/mm at an MTF of 0.2. The current study has further refined the luminescence mechanism of Mn(II)‐based materials and optimizes their properties for a wider range of applications.

Transformative Impact of AI and Cloud Technologies: A Comparative Analysis across Healthcare, Retail, and Mobile Financial Services

This article examines the transformative impact of artificial intelligence (AI) and cloud technologies across healthcare, retail, and mobile financial services sectors. Through a comprehensive analysis of industry-specific applications, we explore how these technologies are revolutionizing operational efficiency, customer experiences, and innovation in each domain. In healthcare, we investigate the role of CI/CD automation and scalable cloud infrastructure in improving system reliability and patient care. The retail sector analysis focuses on AI-driven customer insights and supply chain optimization. For mobile financial services, we examine AI applications in fraud detection and real-time analytics. The findings reveal significant improvements in operational efficiency, decision-making processes, and customer satisfaction across all three industries. However, we also identify unique challenges and opportunities specific to each sector. This article contributes to the growing body of literature on digital transformation and provides valuable insights for industry professionals and policymakers navigating the integration of AI and cloud technologies in their respective fields.

Nanotechnology in retinal diseases: From disease diagnosis to therapeutic applications.

Nanotechnology has demonstrated tremendous promise in the realm of ocular illnesses, with applications for disease detection and therapeutic interventions. The nanoscale features of nanoparticles enable their precise interactions with retinal tissues, allowing for more efficient and effective treatments. Because biological organs are compatible with diverse nanomaterials, such as nanoparticles, nanowires, nanoscaffolds, and hybrid nanostructures, their usage in biomedical applications, particularly in retinal illnesses, has increased. The use of nanotechnology in medicine is advancing rapidly, and recent advances in nanomedicine-based diagnosis and therapy techniques may provide considerable benefits in addressing the primary causes of blindness related to retinal illnesses. The current state, prospects, and challenges of nanotechnology in monitoring nanostructures or cells in the eye and their application to regenerative ophthalmology have been discussed and thoroughly reviewed. In this review, we build on our previously published review article in 2021, where we discussed the impact of nano-biomaterials in retinal regeneration. However, in this review, we extended our focus to incorporate and discuss the application of nano-biomaterials on all retinal diseases, with a highlight on nanomedicine-based diagnostic and therapeutic research studies.

Review on peak detect and hold circuits and their applicability in partial discharge detection

Abstract This article reviews various designs of electrical peak detect and hold circuits and tests their applicability for the detection of partial discharge pulses. Since partial discharges are very short pulses in the nanosecond range, the peak detector circuit must be very fast and thus work precisely over a wide bandwidth up to the GHz range. The article begins with a comprehensive literature review and gives an overview of the state of the art of peak detector circuits. As a result of the literature research, four different peak detector designs are identified that appear suitable for measuring partial discharges. These four circuits are then simulatively tested for their performance in measuring the pulse height of various partial discharge pulses. It turns out that two of the identified circuits are able to measure nanosecond pulses of small amplitude. These two circuits must be further optimized for partial discharge detection in the future. In addition, the circuit should be tested prototypically with real partial discharge pulses.

Developing an AI-based application for caries index detection on intraoral photographs.

This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University Hospital, Karachi, Pakistan, this study utilized a dataset comprising 7,465 intraoral images, including both primary and secondary dentitions. These images were meticulously annotated by two experienced dentists and further verified by senior dentists. A YOLOv5s model was trained on this dataset and integrated into a smartphone application, while a Detection Transformer was also fine-tuned for comparative purposes. Explainable AI techniques were employed to assess the AI's decision-making processes. A sample of 70 photographs was used to directly compare the application's performance with that of junior dentists. Results showed that the YOLOv5s-based smartphone application achieved a precision of 90.7%, sensitivity of 85.6%, and an F1 score of 88.0% in detecting dental decay. In contrast, junior dentists achieved 83.3% precision, 64.1% sensitivity, and an F1 score of 72.4%. The study concludes that the YOLOv5s algorithm effectively detects dental decay on intraoral photographs and performs comparably to junior dentists. This application holds potential for aiding in the evaluation of the caries index within populations, thus contributing to efforts aimed at reducing the disease burden at the community level.

Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA–ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA–ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

Ultra Enhancement of the Resolution of the Divergence Beam-Based SPR Sensor Using a Wavelet Filter

Surface Plasmon Resonance (SPR) sensors are highly sensitive to refractive index variations, making them ideal for biosensing and chemical detection applications. However, their performance can be constrained by noise, resolution limits, and signal distortions, particularly in divergence beam-based configurations. This research presents an ultra-enhancement of the performance of divergence beam-based SPR sensors by employing advanced wavelet filtering techniques. Wavelets, with their multi-resolution analysis capability, are applied to denoise and refine the sensor signal, significantly improving key performance metrics, including resolution, mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), sensitivity matrix (SM), and combined sensitivity factor (CSF).The wavelet filter effectively decomposes the SPR signal into distinct frequency bands, separating noise while retaining high-frequency components required for accurate sensing. This results in a significant reduction in MSE and RMSE of 30 and 92.26%, respectively, while simultaneously improving SNR by 54.08%, maintaining signal quality. The wavelet filter significantly improved the resolution of the SPR sensor by 99.95% (1.3772×10-7 RIU), allowing for more precise detection of refractive index changes and boosting diverge beam-based SPR sensor performance. In addition, a sensitivity matrix (SM) and combined sensitivity factor (CSF) were improved by 42 and 41.94%, respectively, allowing for a more thorough evaluation of the sensor's performance across multiple operational parameters. Simulation and experimentation show that wavelet filtering surpasses standard filtering approaches in terms of noise suppression and signal clarity. This technology has considerable potential for improving the accuracy and reliability of SPR sensors in high-sensitivity applications

Digital PCR in Virology: Current Applications and Future Perspectives.

Digital PCR (dPCR) has been used in the field of virology since its inception. Technological innovations in microfluidics more than a decade ago caused a sharp increase in its use. There is an emerging consensus that dPCR now outperforms quantitative PCR (qPCR) in the basic parameters such as precision, sensitivity, accuracy, repeatability and resistance to inhibitors. These strengths have led to several current applications in quantification, mutation detection and environmental DNA and RNA samples. In high throughput scenarios, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the cost and throughput still significantly hampered the adaption of dPCR. There is much unexplored potential within the multiplexing capabilities of dPCR. This will allow simultaneous multi-target quantification and can also partially alleviate the throughput and cost drawback. In this review, we discuss the strengths and weaknesses of dPCR with a focus on virology applications and we discuss future applications. Finally, we discuss recent evolutions of the technology in the form of real-time dPCR and digital high-resolution melting.

Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.

Multifunctional CeO2:Fe3+ electrodes: Superior uric acid sensing and high-efficiency supercapacitor application

This study investigates the versatile capabilities of iron-doped CeO2-modified carbon paste electrodes (FCO MCPE) for both enhanced uric acid (UA) detection and efficient energy storage applications. Iron-doped CeO2 nanoparticles were synthesized via combustion synthesis. Cyclic voltammetry (CV) showed a 29 % increase in anodic peak current (2.05 µA) with the FCO MCPE compared to bare carbon paste electrodes (BCPE) (1.59 µA), indicating improved catalytic properties. Analysis of scan rates revealed a diffusion-controlled oxidation process. Differential pulse voltammetry (DPV) demonstrated excellent linearity (R2 = 0.999) and low detection limits (LOD: 0.132 µM, LOQ: 0.442 µM). Selectivity tests confirmed the superior ability of the FCO MCPE to differentiate UA from dopamine (DA), with a high linearity (R2 = 0.998). Stability tests showed only an 11 % decrease in performance after 15 cycles. In super capacitor evaluations, FCO materials exhibited a high specific capacitance of 200.43 F g−1 at 2 mV/s and impressive cycling stability with 87.5 % capacitance retention after 5000 cycles. Electrochemical impedance spectroscopy (EIS) revealed efficient charge transfer with a peak specific capacitance of 52.39 F g−1 at 0.01 Hz. These findings underscore the potential of FCO MCPEs for both effective UA detection and efficient super capacitor applications.

Microwave enhanced carbon dots synthesis from eggshell membrane: Versatile applications in heavy metal ion sensing, strain free detection of fingerprints, UV shielding, food packing and anti-counterfeiting

In this study, a one-step microwave irradiation process combined with a sodium hydroxide (NaOH) solution allowed for the quick and easy synthesis of carbon dots (CDs) from eggshell membrane (ESM) ashes. The resultant CDs demonstrated exceptional selectivity for Fe3+ and excellent fluorescence (FL) with a quantum yield (QY) of 15.3%. Utilizing synthetic CDs, a sensitive nanoprobe is utilized to identify free Fe3+ within the 0-350μM (R2 = 0.9976) range, with a 0.281μM limit of detection (LoD). Additionally, the CDs were shown to be useful for intracellular Fe3+ detection applications because of their low cytotoxicity and biocompatibility. Investigations using real samples at 420 and 500nm excitation wavelengths showed promising results in terms of real-world application. In order to transform molecular data into FL signal outputs, a multi-input logic gate is also constructed. In the UV-A (93%), UV-B (88%), UV-C (98%) and HEBL (79%) regions, blue colour-emitting CDs had the maximum UV blockage, but pure polyvinyl alcohol (PVA) absorbed less than 22–30% of the light in all UV regions. The integration of CDs into the polymer improved the thermal characteristics of the PVA film. Additionally, testing of in vitro cell viability demonstrated that the CDs, when embedded in the PVA, did not cause cytotoxicity to the Neuroblastoma cell line (SH-SY-5Y). As a paradigm for perishable commodities, the effects of CDs and PVA were assessed on the shelf life of tomatoes. When fresh tomatoes were coated with CDs@PVA and PVA, weight and moisture loss were effectively decreased, according to the results of the long-term monitoring study. Throughout almost 34 days at room temperature, it also dramatically reduced the growth of fungi and prevented spoiling, all the while maintaining the fruits original colour and appearance. The CDs FL under 365nm UV light excitation makes them well-suited for creating fluorescent inks to deter counterfeiting. Additionally, the study explored the CDs exceptional FL properties for use as a luminescent fingerprint powder, aiding in the detection of latent fingerprints (LFPs) on various surfaces. The findings from this study highlight the potential of cost-effective and safe CDs for a range of applications, including non-cytotoxic UV blocking, active packaging, Fe3+ sensing, fingerprint detection, anti-counterfeiting (AC) measures and flexible nanocomposite (NC) films.

Synthesis of Yellow-Emitting Fluorescent Aniline-Doped Ellagic Acid via C-N Coupling Reaction and Its Application in the Selective Detection of Metal Ions

Ellagic acid (EA), a natural polyphenol, features a highly organized planar structure with an extensive sp2 carbon-conjugated area, making it favorable for light energy absorption and fluorescent emission. However, this planar structure can easily aggregate through π-π stacking, leading to aggregation-caused quenching of fluorescence. In this research, aniline-doped ellagic acid (EAAn2) was successfully synthesized via a C-N coupling reaction to address this issue. The fluorescence properties of EAAn2 in various organic solvents were thoroughly investigated. Compared to EA, EAAn2 exhibited a red shift in fluorescence emission, primarily revealing yellow fluorescence instead of the green fluorescence of EA. Moreover, the fluorescence intensity and the concentration at which aggregation-caused quenching occurs for EAAn2 were substantially improved due to the two grafted aniline molecules.EAAn2 was utilized as a molecular fluorescent probe in DMSO to detect three kinds of metal ions: alkali and alkaline-earth metal ions, transition metal ions, and post-transition metal ions, using EA as a control. It was found that the combination of EAAn2 with these metal ions exhibited stronger fluorescence intensity than EA and effectively resisted the blue shift in fluorescence emission due to aniline doping. Based on these results, Mg2⁺ ions could be clearly distinguished from other alkali and alkaline-earth metal ions by differences in their fluorescence emission, as could Cr2⁺ ions among transition metals. This research effectively enhances the fluorescence properties of EA through C-N coupling and demonstrates its greater potential in the detection of metal ions, providing fundamental and valuable information for future fluorescent applications.

AI in Agriculture : Revolutionizing Precision Farming and Sustainable Crop Management

This article explores the transformative impact of Artificial Intelligence (AI) on agriculture, focusing on precision farming and sustainable crop management. It highlights how AI-driven technologies are revolutionizing agricultural practices by optimizing resource utilization, enhancing productivity, and promoting sustainability. The article discusses key applications of AI in agriculture, including crop health monitoring, predictive analytics, resource optimization, smart irrigation systems, variable rate application of chemicals, and pest and disease detection. It presents case studies and statistical data demonstrating the significant improvements in crop yields, resource efficiency, and environmental sustainability achieved through AI adoption. The article also addresses the challenges facing widespread AI implementation in agriculture, such as cost barriers, lack of technical expertise, data quality issues, and infrastructure limitations, while exploring future directions including the integration of AI with robotics and blockchain technology.

Developing surface plasmon resonance imaging for discrete particle detection based on a silver layer coated with polyacrylic acid/iodine polyelectrolyte brushes

The detection and analysis of low concentrations of chemical and biological particles present an enduring challenge in scientific exploration. Among the various techniques employed for this purpose, surface plasmon resonance (SPR) stands as a powerful label-free method with wide-range applications in advanced detection and sensing. Traditional SPR, while highly effective, encounters inherent limitations when it comes to scrutinizing individual particles. To overcome this limitation, wide-field surface plasmon resonance microscopy emerges as a promising approach, offering real-time detection capabilities for suspended particles in solution. In this study, we present an innovative wide-field surface plasmon resonance microscope, strategically combining a silver layer coated with polyelectrolyte brushes—polyacrylic acid/iodine to enhance the detection sensitivity and mitigate silver’s susceptibility to oxidation. It was demonstrated that coating the silver layer with polyacrylic acid/iodine enhances the sensitivity of discrete particle imaging with high spatial resolution of the recorded image. Since wide-field surface plasmon resonance microscopy can detect discrete particles, we present a mathematical model to describe the SPR sensing mechanism based on discrete particles for precisely characterizing and interpreting our experimental observations. The work demonstrates a capability for comprehensive analysis of low concentrations of chemical and biological particles at the single particle level.

Highly Responsive Cryogel Based Sensing Platform by Encapsulating Programmed DNA for Colorimetric Detection of 17β-estradiol

Responsive hydrogels show obvious superiorities when being employed at detection owing to their polymeric networks and optical properties. Albeit important, the slow response of traditional hydrogels against targets limits their further applications, and enhancing the responsiveness of hydrogel is therefore imperative. Herein, a cryogel with hierarchical structures was designed through adjusting the hydrogel structure by the freezing process to improve the responsive properties, and fabricated for rapid analysis of 17β-estradiol (E2). The cryogel encapsulating the DNA hairpins rapidly adsorbed the complementary DNA isolated from the aptamer in the presence of E2 and activated an efficient hybridization chain reaction (HCR). After integration with a smartphone system, a portable colorimetric sensing platform was created for sensitive detection of E2 with a low detection limit of 0.5 nM. Our work provides a promising way for highly responsive hydrogel-based biosensor design, and broadens their applications in rapid and portable target detection.