Smart Visual Fluorescence Sensing of Tetracycline Using N-Doped Nb 2 C Quantum Dots and CNN-Based Regression Quantification

Fluorescence quantum dots (QDs) are promising functional nanomaterials in chemical biology and environmental applications, where an analyte-induced responsive system is beneficial for detecting numerous life-related molecules and pollutants. Here, fluorescent Ti3C2 MXene quantum dots (MQDs) with the size of 1.75 nm were synthesized by a simple method of hydrofluoric acid etching and dimethyl sulfoxide exfoliation to form nanosheets followed by a one-step ultrasound method. The as-synthesized MQDs showed excitation-dependent behaviour along with a fluorescence quantum yield value of 7.7%. In addition, the fluorescence of the MQDs can be significantly suppressed by Fe3+. The mechanism for the fluorescence quenching of the MQDs was systematically investigated, which was attributed to the oxidation-reduction reaction between the MQDs and Fe3+ and the inner filter effect (IFE), different from the reported Förster resonant energy transfer (FRET) mechanism for MXene nanosheets. Based on this trait, a fluorescence method for Fe3+ detection based on MQDs was demonstrated with high sensitivity and selectivity, and the limit of detection was 310 nM. The proposed method was successfully used for the sensitive detection of Fe3+ in serum and sea water. This work will not only help to understand the selectivity mechanisms of MQDs as fluorescent probes for metal ions, but also provide a smart sensing platform in biological and environmental detection.

Highly stable Cs4PbBr6/CsPbBr3perovskite nanoparticles as a new fluorescence nanosensor for selective detection of trace tetracycline in food samples

A dual-response ratiometric fluorescent sensor by europium-doped silicon nanoparticles for fluorescent and smartphone imaging detection of tetracycline

A novel functionalized nitrogen- and sulfur-co-doped nanocarbon dots for the fluorescence detection and photocatalysis degradation of tetracycline antibiotics.

A highly sensitive and accurate dual-signal strategy is developed for trace Cu2+ detection based on the inner filter effect (IFE) between Tween 20-gold nanoparticles (AuNPs) and CdSe/ZnS quantum dots (QDs). Tween 20-AuNPs are utilized as colorimetric probes and excellent fluorescent absorbers. The fluorescence of CdSe/ZnS QDs can be quenched efficiently by Tween 20-AuNPs via IFE. In the presence of D-penicillamine, D-penicillamine induces the aggregation of Tween 20-AuNPs and the fluorescent recovery of CdSe/ZnS QDs at high ionic strength. Upon addition of Cu2+, D-penicillamine tends to selectively chelate with Cu2+ and then forms the mixed-valence complexes, which consequently inhibits the aggregation of Tween 20-AuNPs and the fluorescent recovery. The dual-signal method is used to quantitatively detect trace Cu2+, with low detection limits of 0.57 μg/L and 0.36 μg/L for colorimetry and fluorescence, respectively. In addition, the proposed method using a portable spectrometer is applied to the detection of Cu2+ in water. This sensitive, accurate and miniature sensing system has potential in environmental evaluations.

A molecularly imprinted fluorescence sensor for sensitive detection of tetracycline using nitrogen-doped carbon dots-embedded zinc-based metal-organic frameworks as signal-amplifying tags

Nitrogen-doped carbon quantum dots as a highly selective fluorescent and electrochemical sensor for tetracycline

A dual-mode fluorescence and colorimetric biosensor based on nitrogen-boron co-doped carbon quantum dot (N-B CQDs) for rapid and sensitive detection of dopamine (DA) was developed. The quantum dot luminescent materials, N-B CQDs, were prepared by a one-step microwave-assisted method. The N-B CQDs were characterized using SEM, HR-TEM, XRD, FT-IR, Raman, fluorescence, and UV-Vis techniques. The dual-mode assays of fluorescence and colorimetric methods were used for detection of DA. The high fluorescent N-B CQDs mediated turn-off assay for the facile room temperature detection of dopamine via inner filter effect (IFE) and Forster resonance energy transfer (FRET) processes atbasic pH. The colorimetric detection of DA was also developed via in-house android application using a smartphone and N-B CQD solution-based nanosensor. The smartphone-based colorimetric biosensors generated morereliable information for quantitative analysis of color changes than the naked eye. Furthermore, a smartphone application with N-B CQD solution-based nanosensor was integrated to monitor the color changes through the DA addition. Wide linear ranges were achieved for DA in the ranges 0.25-50μM and 5-500μM with fluorescence and smartphone-based method, respectively. The satisfactory results of the dual-mode detection of DA, not only in aqueous solution, but also in human urine and serum biological sample demonstrated its potential application in biosensing, asa point of care diagnostic tool. Graphical abstract.

Recognition of human activity based on convolutional neural network has received the interest of researchers in recent years due to its significant improvement in accuracy. A large number of algorithms based on the deep learning approach have been proposed for activity recognition purpose. However, with the increasing advancements in technologies having limited computational resources, it needs to design an efficient deep learning-based approaches with improved utilization of computational resources. This paper presents a simple and efficient 2-Dimensional convolutional neural network (2-D CNN) architecture with very small size convolutional kernel for human activity recognition. The merit of the proposed CNN architecture over standard deep learning architectures is fewer trainable parameters and lesser memory requirement which enables it to train the proposed CNN architecture on low GPU memory-based devices and also works well with smaller as well as larger size datasets. The proposed approach consists of mainly four stages: namely (1) creation of dataset and data augmentation, (2) designing 2-D convolutional neural network (CNN) architecture, (3) the proposed 2-D CNN architecture trained from scratch up to optimum stage, and (4) evaluation of the trained 2-D CNN architecture. To illustrate the effectiveness of the proposed architecture several extensive experiments are conducted on three publicly available datasets, namely IXMAS, YouTube, and UCF101 dataset. The results of the proposed method and its comparison with other state-of-the-art methods [8-12,14,18-26,29-33] demonstrate the usefulness of the proposed method.

A facile nickel nanocluster-based fluorescent ‘‘turn-off” sensor to detect tetracycline antibiotics

A novel reversible-responsive ratiometric fluorometry based on an anthracene-based imidazolium ionic liquid and Eu3+-based lanthanide coordination polymers for selective sensing tetracycline in foods and waters.

Carbon dots (CDs) possess unique optical properties such as tunable photoluminescence (PL) and excitation dependent multicolor emission. The quenching and recovery of the fluorescence of CDs can be utilized for detecting analytes. The PL mechanisms of CDs have been discussed in previous articles, but the quenching mechanisms of CDs have not been summarized so far. Quenching mechanisms include static quenching, dynamic quenching, Forster resonance energy transfer (FRET), photoinduced electron transfer (PET), surface energy transfer (SET), Dexter energy transfer (DET) and inner filter effect (IFE). Following an introduction, the review (with 88 refs.) first summarizes the various kinds of quenching mechanisms of CDs (including static quenching, dynamic quenching, FRET, PET and IFE), the principles of these quenching mechanisms, and the methods of distinguishing these quenching mechanisms. This is followed by an overview on applications of the various quenching mechanisms in detection and imaging.

In this paper, we study the performance of variants of well-known Convolutional Neural Network (CNN) architectures on different audio tasks. We show that tuning the Receptive Field (RF) of CNNs is crucial to their generalization. An insufficient RF limits the CNN's ability to fit the training data. In contrast, CNNs with an excessive RF tend to over-fit the training data and fail to generalize to unseen testing data. As state-of-the-art CNN architectures - in computer vision and other domains - tend to go deeper in terms of number of layers, their RF size increases and therefore they degrade in performance in several audio classification and tagging tasks. We study well-known CNN architectures and how their building blocks affect their receptive field. We propose several systematic approaches to control the RF of CNNs and systematically test the resulting architectures on different audio classification and tagging tasks and datasets. The experiments show that regularizing the RF of CNNs using our proposed approaches can drastically improve the generalization of models, out-performing complex architectures and pre-trained models on larger datasets. The proposed CNNs achieve state-of-the-art results in multiple tasks, from acoustic scene classification to emotion and theme detection in music to instrument recognition, as demonstrated by top ranks in several pertinent challenges (DCASE, MediaEval).

Every year, new Convolutional Neural Network (CNN) architectures appear to deal with different problems in the activity of image and video recognition. These architectures usually work along the ImageNet dataset for looking for the best performance of the CNNs without taking into account the video task where they are used. This can represent a problem if the task is Human Action Recognition (HAR) in video, since the CNN architectures are pre-trained with an image dataset that can practically contain any object, while HAR problem requires consecutive frames of people doing actions. To prove the idea that using CNNs pre-trained on an image dataset does not always achieve the best performance on a video dataset and that, therefore, it is worth comparing the performance of different CNNs under similar circumstances for the HAR problem, this work proposes an analysis between eight different CNN architectures. Each one of the CNN was exclusively trained with RGB images, which were extracted from the frames of the different classes of videos of HMDB51 dataset. To make the classification of an activity in video, we average the predictions taking into account the successes. We also made some ensembles with the best performance CNNs to measure the improvement in accuracy. Our results suggest that Xception is a strong baseline model that could be used by the community to make their comparisons of their proposals more robust.

As a kind of antibiotic, tetracycline (TC) might remain in animal blood and milk products during use, which poses a risk to humans after consumption. Therefore, a ratiometric fluorescence probe was proposed for the detection of TC, which was based on anEu3+ functionalized hydrogen-bonded organic framework (HOF). Since there are alarge number of N and O atoms in the skeleton of HOF, more Eu3+ could be loaded onto HOF by forming coordinate bonds, while preserving the fluorescence of luminol monomer in HOF. In the presence of TC, the fluorescence of luminol monomer was attenuated at 425nm due to inner filter effect (IFE), while TC selectively enhanced the fluorescence peak at 617nm of Eu3+ under the influence of antenna effect (AE). This highly sensitive probe could detect TC in the range of0.1-60μM and had a low limit ofdetection of 8.51nM. Besides, the HOF@Eu probe was able to detect TC in actual samples (milk and tap water) with good recoveries (95.09%-111.51%) and precision (R < 4.78%), indicating this probe has great application potential forthe detection of TC in food.