Strong Background Interference Research Articles

Background-Free Imaging of Food Freshness Using Curcumin-Functionalized Upconversion Reversible Hydrogel Patch.

Functionalized upconversion nanomaterials can overcome the drawbacks faced of strong background interference, photodamage, and spectral overlap by conventional optical labeling. Here, curcumin-functionalized upconversion hydrogel patch is designed with background-free and reversible for food freshness monitoring by ultra-sensitive response to biogenic amines. By loading the probes onto hydrogel patch, utilizing the good ductility to solve the problem of non-smooth surface coverage, thus accurately capturing biogenic amines. The presence of biogenic amines leads to the conversion of the diketone group on the probe to enolate ions, which triggers fluorescence resonance energy transfer (FRET) and ultimately causes the upconverted fluorescence to gradually change from green to red. The probe exhibits good detection capability for biogenic amines with a low limit of detection (LOD) of 2.73 µm. Interestingly, the patch can be restored to its initial state after water rinsing, realizing reversible detection of biogenic amines. Additionally, combining the color recognition system of smartphone can convert the imaging signal into a data signal to achieve quantitative analysis and show a reliable assessment comparable to the results of high performance liquid chromatography (HPLC). This study demonstrates the practical applicability in real-time monitoring of freshness, suggests great potential in developing optical nano-sensing strategy to ensure food safety.

A highly specific two-photon fluorescent probe for real-time monitoring of acetylcholinesterase in neurogenic disorders in vivo

Acetylcholinesterase (AChE) hydrolyses choline into thiocholine, which is essential for cholinergic neurons to revert to their resting state following activation. Abnormal changes in AChE activity can directly affect nervous system function. Thus, the specific detection of AChE activity is urgently needed for elucidating the function of the nervous system and diagnosing AChE-related diseases. Current methods for detecting AChE activity have several limitations, including strong background interference and poor tissue penetration. Thus, we designed and synthesized a two-photon (TP) excited fluorescent probe, WZ-AChE, for the specific detection of AChE. Briefly, a carbamate bond was chosen to specifically recognize AChE, which can also be cleaved by AChE. The product, WZ, released strong deep red fluorescence signal under TP excitation at 800 nm. Our results showed that WZ-AChE can detect AChE activity in PC12 cells with both superior sensitivity and selectivity. In addition, we successfully applied WZ-AChE to a C. elegans Parkinson's disease (PD) model and a mouse model of depression. The findings revealed that AChE activity was greater in both disease models than in the control group. To summarize, a novel tool was created to investigate the mechanisms underlying PD and depression.

Silver amplified immunosensor via effective fluorogenic Ag+-imidazole aggregation for detection of AFB1

BackgroundCereals are susceptible to aflatoxin contamination during storage and transportation, which is highly carcinogenic and teratogenic, and seriously threaten human health. The accurate and rapid detection of total aflatoxin (including aflatoxin B1, B2, G1, and G2) is of great importance for food safety. Conventional fluorescence immunoassays have the advantage of being sensitive and fast; however, these methods can be affected by strong background and matrix interference. Therefore, the development of ultrasensitive, cost-effective, and interference rejection sensors for detecting aflatoxins in moldy grains is vital for food safety and human health. ResultsIn this paper, a broad-spectrum aflatoxin monoclonal antibody was prepared by using hybridoma cell fusion technology. An aggregation-induced emission (AIE) based immunosensor via silver amplification coupled with a fluorogenic Ag+ probe was established for AFB1 analysis. Silver nanoparticles are decomposed into numerous Ag+ by H2O2, and then Ag+ further specifically binds with imidazole-modified AIE molecules, improving the sensitivity and anti-interference ability of the method. The IC50 and IC15 of AIE-based immunosensor for AFB1 were 0.019 and 0.0014 μg/L, respectively, 2.3-fold and 5.8-fold higher than those of icELISA. The AIE-based immunosensor was also used to analyze AFB1 from actual cereal samples, with spiked recoveries ranging from 72.91 to 115.92 %. In addition, the method was used to detect total aflatoxins in moldy grains. SignificanceBased on the advantages of broad-spectrum aflatoxin monoclonal antibody, high-efficiency metal signal amplification, and functional AIE molecule, a sensitive, accurate, cost-effective, and time-saving method was developed for the analysis of total aflatoxins in cereals. Moreover, the proposed signal amplification strategy shows great potential for analyzing other trace-level small molecular pollutants.

Tailoring near-infrared amyloid-β probes with high-affinity and low background based on CN and amphipathic regulatory strategies and in vivo imaging of AD mice

Amyloid-β (Aβ) species (Aβ fibrils and Aβ plaques), as one of the typical pathological markers of Alzheimer's disease (AD), plays a crucial role in AD diagnosis. Currently, some near-infrared I (NIR I) Aβ probes have been reported in AD diagnosis. However, they still face challenges such as strong background interference and the lack of effective probe design. In this study, we propose molecular design strategy that incorporates CN group and amphiphilic modulation to synthesize a series of amphiphilic NIR I Aβ probes, surpassing the commercial probe ThT and ThS. Theoretical calculations indicate that these probes exhibit stronger interaction with amino acid residues in the cavities of Aβ. Notably, the probes containing CN group display the ability of binding two distinct sites of Aβ, which dramatically enhanced the affinity to Aβ species. Furthermore, these probes exhibit minimal fluorescence in aqueous solution and offer ultra-high signal-to-noise ratio (SNR) for in vitro labeling, even in wash-free samples. Finally, the optimal probe DM-V2CN-PYC3 was utilized for in vivo imaging of AD mice, demonstrating its rapid penetration through the blood-brain barrier and labelling to Aβ species. Moreover, it enabled long-term monitoring for a duration of 120 min. These results highlight the enhanced affinity and superior performance of the designed NIR I Aβ probe for AD diagnosis. The molecular design strategy of CN and amphiphilic modulation presents a promising avenue for the development Aβ probes with low background in vivo/in vitro imaging for Aβ species.

Improved method for drone sound event detection system aiming at the impact of background noise and angle deviation

Drones pose a hidden threat to public safety, and effective and accurate detection technology for the drone that exist in the environment is imminent, in which how to weaken the influence of target sound source localization deviation and strong background interference noise on the detection task is the key to improve the accuracy of drone sound event detection. In this paper, a method has been proposed to improve the accuracy of drone acoustic event detection, named as the linear shrinkage-subspace projection-power spectral density filter method (LSP). This method mainly including covariance matrix reconstruction, steering vector recalibration, and filter coefficient redesign method. Firstly, based on Minimum Variance Distortionless Response, the linear shrinkage method is used to suppress the interference and noise components in the signal plus interference covariance matrix, and the sample covariance matrix is reconstructed to eliminate background interference noise. Then, the correlation between the steering vector and the eigenvector is used to eliminate the angle correlation term, and the subspace projection method is combined to recalibrate the steering vector, so as to improve the ability of the beamforming method to resist the angle deviation and realize the correction of the target source positioning deviation. Next, a redesign method for wiener filter coefficients based on the estimated power spectral density is used to further weaken background interference noise. In order to verify the accuracy of the proposed method, a complete drone sound event detection system is constructed by combining the deep learning drone sound event detection classifier, and the evaluation is carried out according to different angular deviations and interference sound distances. In addition, a new evaluation criterion is proposed, named as the Machine-Human Extreme Hearing Distance Rate (MHDR), which analogizes the system's detection ability with the ear's auditory detection ability. The research results of this article indicate that the detection accuracy of the detection system shows satisfactory accuracy when the proposed method is applied to circular microphone array, that improved by 15.12 % compared to existing methods. The proposed method improves the detection accuracy of the drone acoustic event detection task under the influence of sound source position deviation and strong background speech interference, and provides a reference for the development of anti-drone technology.

An Improved U-Net Infrared Small Target Detection Algorithm Based on Multi-Scale Feature Decomposition and Fusion and Attention Mechanism.

Infrared small target detection technology plays a crucial role in various fields such as military reconnaissance, power patrol, medical diagnosis, and security. The advancement of deep learning has led to the success of convolutional neural networks in target segmentation. However, due to challenges like small target scales, weak signals, and strong background interference in infrared images, convolutional neural networks often face issues like leakage and misdetection in small target segmentation tasks. To address this, an enhanced U-Net method called MST-UNet is proposed, the method combines multi-scale feature decomposition and fusion and attention mechanisms. The method involves using Haar wavelet transform instead of maximum pooling for downsampling in the encoder to minimize feature loss and enhance feature utilization. Additionally, a multi-scale residual unit is introduced to extract contextual information at different scales, improving sensory field and feature expression. The inclusion of a triple attention mechanism in the encoder structure further enhances multidimensional information utilization and feature recovery by the decoder. Experimental analysis on the NUDT-SIRST dataset demonstrates that the proposed method significantly improves target contour accuracy and segmentation precision, achieving IoU and nIoU values of 80.09% and 80.19%, respectively.

Multi-component quantitative analysis of LIBS using adaptively optimized multi-branch CNN

Multi-component quantitative analysis of laser-induced breakdown spectroscopy (LIBS) is very important for industrial process control, food safety and space exploration. Due to the strong background interference and complex matrix effect, traditional quantitative analysis methods are difficult to realize accurate multi-component regression. Deep learning has the advantages of automatically extracting full-spectrum features and strong nonlinear modeling capabilities, which provides new opportunities for advancing the progress of LIBS quantitative analysis. However, due to the large difference of spectral line intensity and spectral cross-interference, existing deep learning models have limited performance for simultaneous multi-component regression. To this end, a novel adaptively optimized multi-branch convolution neural network (AOMB-CNN) framework is proposed for multi-component quantitative analysis of LIBS. Multi-branch structure is adopted to ensure the accuracy of multi-component content simultaneously. Dynamic weighted loss function and Bayesian optimization algorithm are used to adaptively optimize the deep learning model. The results demonstrate that the AOMB-CNN can effectively improve the performance of multi-component regression on 2 LIBS datasets compared with the traditional quantitative analysis methods. The root mean square error (RMSE) of 8 components of the ChemCam test set are 2.4465, 0.3486, 1.2481, 1.3909, 0.9010, 0.8892, 0.6700, and 0.5498 respectively, and the RMSE of 4 components of the steel test sets are 1.9787, 0.3455, 0.6025 and 1.6631 respectively. The proposed method provides an end-to-end, adaptively optimized, full-spectrum deep learning solution for LIBS multi-component content measurement without human intervention.

Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles

Addressing the issues of weak echo signals and strong background interference in the laser detection of ships’ wakes, an analysis of the laser backscatter detection characteristics of ships’ wakes has been conducted. Based on the Monte Carlo method, a simulation model for the dual-mode fusion detection of ship wake bubbles using laser technology was constructed under different target characteristics. A dual-mode fusion detection system for ships’ wakes was designed, and an indoor experimental platform for the dual-mode fusion detection of ship wake bubbles using laser technology was established. To address problems such as a wide range of echo signal intensity changes, severe signal fluctuations, low resolution, poor image contrast, and blurred target edge information, an algorithm based on multi-timescale hierarchical fusion signal processing and temporal difference accumulation image processing was proposed. Verification experiments for ship wake detection were conducted, which revealed that the dual-mode fusion detection method for ship wake bubbles using laser technology can effectively enhance the detection signal-to-background ratio and counter the maneuvering evasion of underwater weapons by ships. It achieved high sensitivity, large dynamic range, high resolution, and a wide field of view detection and real-time signal processing of ship wake bubble targets of different magnitudes against a strong reverberation background. The effectiveness of the dual-mode fusion detection mode was validated, providing theoretical support for the overall system design and parameter settings.

Infrared-modulated photoluminescence spectroscopy: from wide-band coverage to micro-area and high-throughput scanning imaging

Photoluminescence (PL) spectroscopy has been widely used in the ultraviolet-near-infrared spectral range for over seventy years since the very early report in 1950’s, because it not only reveals the electronic structure information of, e.g., band gap and impurity energy levels of semiconductor materials, but also serves as an efficient tool for analyzing interfacial structures, carrier lifetime, and quantum efficiency. In the infrared band beyond about 4 μm, however, the study of PL spectroscopy had been limited for decades long due to strong thermal background interference, weak PL signal and low detection ability. In this review, a conventional PL method is introduced based on a Fourier transform infrared (FTIR) spectrometer, and a continuous-scan FTIR spectrometer-based double-modulation PL (csFTIR-DMPL) method is briefly described that was proposed in 1989 for breaking through the dilemma of the infrared band, and developed continuously in the later more than 20 years, with its limitations emphasized. Then, a step-scan FTIR spectrometer-based infrared modulated PL (ssFTIR-MPL) method reported in 2006 is analyzed with highlights on its advantages of anti-interference, sensitivity and signal-to-noise ratio, followed by enumerating its effectiveness demonstration and application progress in many research groups worldwide. Further developments in recent years are then summarized of wide-band, high-throughput scanning imaging and spatial micro-resolution infrared modulated PL spectroscopic experimental systems, and the technological progresses are demonstrated of infrared-modulated PL spectroscopy from 0.56-20 μm visible-far-infrared broadband coverage to > 1k high-throughput spectra imaging and ≤2-3 μm spatial micro-resolution. Typical achievements of collaborative research are enumerated in the visible-far-infrared semiconductor materials of dilute nitrogen/dilute bismuth quantum wells, HgCdTe epitaxial films, and InAs/GaSb superlattices. The results presented demonstrate the advancement of infrared modulated PL spectroscopy and the effectiveness of the experimental systems, and foresee further application and development in the future.

Joint Detection and Reconstruction of Weak Spectral Lines under Non-Gaussian Impulsive Noise with Deep Learning

Non-Gaussian impulsive noise in marine environments strongly influences the detection of weak spectral lines. However, existing detection algorithms based on the Gaussian noise model are futile under non-Gaussian impulsive noise. Therefore, a deep-learning method called AINP+LR-DRNet is proposed for joint detection and the reconstruction of weak spectral lines. First, non-Gaussian impulsive noise suppression was performed by an impulsive noise preprocessor (AINP). Second, a special detection and reconstruction network (DRNet) was proposed. An end-to-end training application learns to detect and reconstruct weak spectral lines by adding into an adaptive weighted loss function based on dual classification. Finally, a spectral line-detection algorithm based on DRNet (LR-DRNet) was proposed to improve the detection performance. The simulation indicated that the proposed AINP+LR-DRNet can detect and reconstruct weak spectral line features under non-Gaussian impulsive noise, even for a mixed signal-to-noise ratio as low as −26 dB. The performance of the proposed method was validated using experimental data. The proposed AINP+LR-DRNet detects and reconstructs spectral lines under strong background noise and interference with better reliability than other algorithms.

An enhanced empirical Fourier decomposition method for bearing fault diagnosis

To address the problem that bearing fault signals are usually contaminated by strong background interference due to multiple structures and complex transmission paths, which affects accurate fault feature extraction, an enhanced empirical Fourier decomposition technique was proposed in this paper. First, in order to weaken the influence of transmission path, the trend-line-extraction-based method was utilized in advance, which suppressed the signal distortion and background noise interference. Then, to achieve the appropriate parameter for the empirical Fourier decomposition, the correlation-coefficient-based decomposition number selection approach was constructed to avoid the existence of irrelevant modal functions. The band improvement strategy was proposed to reduce the invalid frequency bands with too narrow bandwidth during the decomposition process, the weighted harmonics significant index was utilized as the target, and the optimal modal components were also determined. Last, the fast Fourier transform was employed, and the bearing fault signatures were accurately detected. The simulation and experimental bearing fault signals were used for analysis; with the help of some comparisons, the analyzed results show that this method can effectively extract the fault characteristics of rolling element bearing from strong background interference.

Plasmonic Scattering Imaging of Surface-Bonded Nanoparticles at the Solution-Solid Interface.

Imaging nanoscale objects at interfaces is essential for revealing surface-tuned mechanisms in chemistry, physics, and life science. Plasmonic-based imaging, a label-free and surface-sensitive technique, has been widely used for studying the chemical and biological behavior of nanoscale objects at interfaces. However, direct imaging of surface-bonded nanoscale objects remains challenging due to uneven image backgrounds. Here, we present a new surface-bonded nanoscale object detection microscopy that eliminates strong background interference by reconstructing accurate scattering patterns at different positions. Our method effectively functions at low signal-to-background ratios, allowing for optical scattering detection of surface-bonded polystyrene nanoparticles and severe acute respiratory syndrome coronavirus 2 pseudovirus. It is also compatible with other imaging configurations, such as bright-field imaging. This technique complements existing methods for dynamic scattering imaging and broadens the applications of plasmonic imaging techniques for high-throughput sensing of surface-bonded nanoscale objects, enhancing our understanding of the properties, composition, and morphology of nanoparticles and surfaces at the nanoscale.

Phase correction based SNR enhancement for distributed acoustic sensing with strong environmental background interference

Fiber-optic distributed acoustic sensing (DAS) systems based on phase-sensitive optical time domain reflectometry (Φ-OTDR) measure acoustic waves by demodulating the phase variations of the Rayleigh backscattering (RBS) signals in a sensing optical fiber. However, in harsh environments, strong environmental background interference, coupled with the interference fading of the RBS, would introduce severe distortions in DAS signal that cannot be corrected or even can be worsened by phase unwrapping, thus resulting in low signal to noise ratio (SNR) and even undetectable signal. In this work, a novel method based on trend prediction is proposed to correct the distortions induced by phase unwrapping error around the fading points. The method is further validated in processing the data acquired from a field test performed in ocean environments using a home-built DAS system. By locating and erasing the distortion points, and then detrending, the acoustic signal buried in strong environmental background interference is retrieved with an SNR improvement greater than 10 dB. The results show that the proposed phase correction method can effectively enhance DAS's SNR for those challenging applications with strong background interference.

AF-OSD: An Anchor-Free Oriented Ship Detector Based on Multi-Scale Dense-Point Rotation Gaussian Heatmap

Due to the complexity of airborne remote sensing scenes, strong background and noise interference, positive and negative sample imbalance, and multiple ship scales, ship detection is a critical and challenging task in remote sensing. This work proposes an end-to-end anchor-free oriented ship detector (AF-OSD) framework based on a multi-scale dense-point rotation Gaussian heatmap (MDP-RGH) to tackle these aforementioned challenges. First, to solve the sample imbalance problem and suppress the interference of negative samples such as background and noise, the oriented ship is modeled via the proposed MDP-RGH according to its shape and direction to generate ship labels with more accurate information, while the imbalance between positive and negative samples is adaptively learned for the ships with different scales. Then, the AF-OSD based on MDP-RGH is further devised to detect the multi-scale oriented ship, which is the accurate identification and information extraction for multi-scale vessels. Finally, a multi-task object size adaptive loss function is designed to guide the training process, improving its detection quality and performance for multi-scale oriented ships. Simulation results show that extensive experiments on HRSC2016 and DOTA ship datasets reveal that the proposed method achieves significantly outperforms the compared state-of-the-art methods.

A Two-Stage Rolling Bearing Weak Fault Feature Extraction Method Combining Adaptive Morphological Filter with Frequency Band Selection Strategy

To extract the weak fault features hidden in strong background interference in the event of the early failure of rolling bearings, a two-stage based method is proposed. The broadband noise elimination ability of an adaptive morphological filter (AMF) and the superior capability of a frequency band selection (FBS) strategy for fault transient location identification are comprehensively utilized by the proposed method. Firstly, the AMF with a simple theory and high calculation efficiency is used as a preprocessing program to enhance the fault transient features. Then, the proposed FBS strategy based on the sparsity index (SI) is utilized to further handle the filtered signal processed by the AMF. Finally, the constructed optimum bandpass filter based on the analysis result of the FBS is used to further filter the handled signal processed by AMF and envelope spectral analysis is applied on the last filtered signal to realize the ideal fault feature extraction effect. Compared with the other traditional FBS methods based on kurtosis or the other index, the proposed FBS strategy based on SI has strong robustness to noise. One experimental signal and one engineering vibration signal are used, respectively, to verify the feasibility of the proposed method.

Improving sparse representation with deep learning: A workflow for separating strong background interference

Revealing hidden reservoirs that are severely shielded by strong background interference (SBI) is critical to subsequent refined interpretation. To enhance the characterization of these reservoirs, current interpretation workflows merge multiple attribute information, necessitating intensive human expertise. As an alternative, we regard SBI suppression as a signal separation problem and develop a workflow to suppress SBI by cascading a sparse representation method and deep learning. SBI has coherent morphological characteristics in seismic sections; reservoir seismic responses, such as channels and karst caves, have a narrow spatial distribution, exhibiting abrupt morphological characteristics. As their morphologies differ, we select two 2D sparse representation dictionaries to identify their individual components. Through the morphological component analysis (MCA) technique, we can obtain adequate SBI separation results. However, the MCA separation is inevitably limited because 2D dictionaries cannot adequately represent 3D structures, but 3D dictionaries are not viable due to computing constraints. As an extension, we use 3D deep learning to improve the separation results based on the 2D MCA results. Specifically, the network is fed with training samples from a region with better SBI suppression results obtained by the MCA method. After learning a direct mapping from noisy data to SBI, the network can improve the separation results and remove more SBI than the previous conventional method. Field data experiments demonstrate that our separation workflow successfully enhances reservoir structures after removing SBI.

Research on Multi-Ship Target Detection and Tracking Method Based on Camera in Complex Scenes

Aiming at the problem that multi-ship target detection and tracking based on cameras is difficult to meet the accuracy and speed requirements at the same time in some complex scenes, an improved YOLOv4 algorithm is proposed, which simplified the network of the feature extraction layer to obtain more shallow feature information and avoid the disappearance of small ship target features, and uses the residual network to replace the continuous convolution operation to solve the problems of network degradation and gradient disappearance. In addition, a nonlinear target tracking model based on the UKF method is constructed to solve the problem of low real-time performance and low precision in multi-ship target tracking. Multi-ship target detection and tracking experiments were carried out in many scenes with large differences in ship sizes, strong background interference, tilted images, backlight, insufficient illumination, and rain. Experimental results show that the average precision of the detection algorithm of this paper is 0.945, and the processing speed is about 34.5 frame per second, where the real-time performance is much better than other algorithms while maintaining high precision. Furthermore, the multiple object tracking accuracy (MOTA) and the multiple object tracking precision (MOTP) of this paper algorithm are 76.4 and 80.6, respectively, which are both better than other algorithms. The method proposed in this paper can realize the ship target detection and tracking well, with less missing detection and false detection, and also has good accuracy and real-time performance. The experimental results provide a valuable theoretical reference for the further practical application of the method.

Detection of Surface Defects in Solar Cells by Bidirectional-Path Feature Pyramid Group-Wise Attention Detector

Due to the multi-scale characteristics of defects and strong background interference, the automation of solar cell surface defect detection is still a challenge. To address this problem, this paper proposes a novel defect ohject detector called BPGA-Detector which consists of two parts: the Bidirectional-Path Feature Pyramid Network (BPFPN) and the Group-wise Attention Module (GAM). The Bidirectional-Path Feature Pyramid Network (BPFPN) combines multiscale features using a bidirectional-path feature fusion method that structured by connecting the bottom-up path feature pyramid network to the original FPN, preserving the characteristics of minor and weak flaws in the shallow layer. Furthermore, the Group-wise Attention Module (GAM) is elaborately designed to suppress the background disturbance and highlight the defect locations by connecting multi-layer contextuai features, which significantly improves the discriminant ability of small defects. Finally, the experimental results on a largescale solar cell dataset including 6263 images, 5763 of which are defective, demonstrate that the proposed method achieve superior detection performance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> up to 88.8%).

Phase Correction Based Snr Enhancement for Distributed Acoustic Sensing with Strong Environmental Background Interference

Phase Correction Based Snr Enhancement for Distributed Acoustic Sensing with Strong Environmental Background Interference

Autofluorescence free detection of carcinoembryonic antigen in pleural effusion by persistent luminescence nanoparticle-based aptasensors

Carcinoembryonic antigen (CEA) is an important biomarker in the diagnosis of cancer. The increase of CEA in malignant pleural effusion appears earlier and possesses higher clinical diagnostic value than that in the serum. Conventional fluorescent probes suffer from the interference of strong biotissue auto-fluorescence, which limits severely their applications in biology detection. Herein, a novel fluorescence aptasensor was designed with near-infrared persistent luminescence nanoparticles (PLNPs) for accurate detection of carcinoembryonic antigen in pleural effusion by FRET quenching and recovery mechanism. The strong background interference from the autofluorescence of pleural effusion samples can be effectively eliminated and extra increments of measured values originated from the background of different samples were ruled out, benefit from the long decay time of PLNPs and time-resolved fluorescence technology. The detection results show high accuracy of the measured values of carcinoembryonic antigen both in cancer and benign disease group with low detection limit up to 0.0851 pg mL−1. Furthermore, excellent selectivity from coexisting biomarker was achieved by the hybridization between the aptamer and the complementary DNA on PLNPs surface. Hence, the established near-infrared PLNPs-based aptasensors offer excellent performance with high selective, accuracy and signal-to-noise ratio for detection of carcinoembryonic antigen in pleural effusion.