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An electrochemical sensor for the rapid, selective and sensitive detection of Pb${}^{2+}$ was developed using polypyrrole-graphene (PPY-rGO) nanocomposite by electrochemical synthesis. The PPY-rGO-modified electrode possessed a large effective area because of unique 3D porous architectures, and it displayed excellent selectivity for the determination of Pb${}^{2+}$. The response of Pb${}^{2+}$ on the nanocomposite-modified electrode was evaluated, and the relevant parameters were optimized by square wave anodic stripping voltammetry (SWASV). The stripping response was highly linear ($R^2=0.987$) over a Pb${}^{2+}$ concentration range of $5\times 10^{-9}$mol/L to $7.5\times 10^{-7}$mol/L, with a detection limit of $4.7\times 10^{-11}$mol/L (3$\sigma$ method). Furthermore, its applicability was validated for the determination of Pb${}^{2+}$ levels using spiked tap water at different concentrations. The polyporous structure, satisfactory reproducibility, long-term stability and simple synthesis endow the PPY-rGO nanocomposite a promising application in the determination of Pb${}^{2+}$.

Herein, a simple-green road for preparing carbon dots (CDs) from the mixture of PEG6000 and papain with approving fluorescence has been successfully developed for the first time. Meanwhile, the current CDs were characterized by analytical means such as fluorescence, UV visible spectrophotometer, HR-TEM, XPS and FTIR and so on. Afterwards, this kind of CDs were employed for analyzing doxycycline (DC) relying on the mechanism that the functional group of DC had interactions with CDs, hence, leading to the fluorescence quenching. Also, this DC analytical method mentioned here permitted in a linear range of $5.0\times 10^{-8}$mol/L-$5.0\times 10^{-4}$mol/L as well as a detection limit of 18nM at a signal-to-noise ratio (S/N) of 3. More importantly, the CDs prepared here were still explored for milk real samples, indicating their potential for broadening avenues towards other various applications.

An electrochemical sensor (Cu${}^{2+}$-IIPs/GCE) was developed for detection of Cu${}^{2+}$ in water. Cu${}^{2+}$-IIPs/GCE was prepared by dispersing Cu${}^{2+}$ imprinted polymers (Cu${}^{2+}$-IIPs) on a preprocessed glassy carbon electrode. Cu${}^{2+}$-IIPs were synthesized on the surface of modified carbon spheres by ion imprinting technology. The electrochemical performance of Cu${}^{2+}$-IIPs/GCE was evaluated by differential pulse voltammetry method. The response of Cu${}^{2+}$-IIPs/GCE to Cu${}^{2+}$ was linear in $1.0\times 10^{-5}$mol/L to $1.0\times 10^{-3}$mol/L. The detection limit was $5.99\times 10^{-6}$mol/L ($S∕N=3$). The current response value of Cu${}^{2+}$-IIPs/GCE was 2.14 times that of the nonimprinted electrode. These results suggest that Cu${}^{2+}$-IIPs/GCE can detect the concentration of Cu${}^{2+}$ in water, providing a new way for heavy metal ions adsorption and testing.

Phenolic compounds, especially 4-nitrophenol (4-NP), and chromium (VI) are highly toxic environmental pollutants. Thus, it is significantly important to establish a rapid and sensitive sensor for 4-NP and Cr(VI) monitoring in environment. In this paper, a novel approach has been adopted where E. coli-derived CDs (CDs-WT) prepared by one step hydrothermal method previously and sensitized by ampicillin (turn-on) were used as fluorescence nanoprobe (CDs-WT-Amp) for 4-NP and Cr(VI) determination based on the inner filter effect quenching mechanism (turn-off). Then, the quenching fluorescence was reversed by addition of ampicillin (turn-on). The linear ranges of 4-NP and Cr(VI) detection were 0–50$\mu$M and 0–25$\mu$M, with the limits of detection 88.89nM and 64.75nM, respectively. The “turn-on-on-off-on” fluorescent nanosensor system was successfully used to determine Cr(VI) and 4-NP in water with satisfactory results. It shows that the “turn-on-off-on” nanoprobe has great promising potential for application to environmental pollutant detection.

There is a broad interest in using graphene or graphene oxide (GO) sheets as a transducer for selective and label-free detection of biomolecules such as DNA, tumor marker, biological ions, etc. Here, a chemical vapor deposition (CVD) graphene-based Hall effect biosensor used for ultrasensitive label-free detection of DNA via DNA hybridization is reported. Hall effect measurements based on the Van der Pauw method are used to perform single-base sequence selective detection of DNA on graphene sheets, which are prepared by CVD. The mobility decreases and the sheet resistance increases with the adding of either complementary or one-base mismatched DNA to the graphene device. The hole carrier concentration of the graphene devices increases apparently with the addition of complementary DNA while it is hardly affected by the one-base mismatched DNA. The detection limit as low as 1pM was realized with a linear range from 1pM to 100nM. Moreover, the Hall effect biosensor was able to distinguish the complementary DNA from one-base mismatched DNA with a high specificity of $\approx$6.2 which is almost two orders of magnitude higher than that of the previously reported graphene biosensors based on DNA–DNA hybridization.

Highly blue fluorescence carbon dots (CDs-MCM) were successfully prepared via a simple hydrothermal method with citric acid and ethylene diamine tetraacetic acid doped mesoporous silica MCM-41. The CDs-MCM exhibited uniform particle size and possessed good excitation-dependent characteristic. It showed highly selectivity and sensitivity in detection of Cr(VI), folic acid and good linear ranges of 0–37.5$\mu$M ($R^2=0.9903$) and 0–50$\mu$M ($R^2=0.9949$). The low detection limits were 79.31nM and 118.73nM for Cr(VI) and folic acid, respectively. It revealed that inner filter effect dominated in Cr(VI) quenching of the CDs-MCM fluorescent, while that of folic acid belonged to static quenching. The CDs-MCM were successfully used to detect the Cr(Vl) and folic acid in water and vegetable samples with satisfactory results. It provided new insight into environmental Cr(VI) and folic acid detection.

In this paper, we prepared a fluorescent sensor based on carbon quantum dots (CDs) and molecularly imprinted technology (MIT) that was successfully synthesized and applied to the detection of aspirin (Asp) residues in biological and pharmaceutical samples. The molecularly imprinted fluorescence sensor had the high sensitivity of quantum dots and the high selectivity of molecularly imprinted polymers (MIPs). Nitrogen-doped carbon quantum dots (N-CDs) with high fluorescence intensity were prepared and then was coated with silicon by the reverse microemulsion method. Finally, 3-aminopropyltriethoxysilane (APTES) was used as the functional monomer and Asp as the template molecule, molecularly imprinted layers were formed around the N-CDs. Under the best experimental conditions, the detection limit of the sensor was 0.198mgL${}^{-1}$. Between 0.9–9.0mgL${}^{-1}$, this fluorescence sensor had a good linear relationship with the concentration of Asp. To test the practicability of the sensor, we used this fluorescent sensor to detect Asp in human urine and saliva. Satisfied recovery rate between 101.1–105.1% and 102.1–106.0% were obtained, respectively.

Multi-walled carbon nanotube (MWCNT)-modified MoS₂/BiVO₄ was manufactured and used for the photoelectrochemical (PEC) detection of hydrogen peroxide (H₂O₂) and hypochlorite (ClO⁻). A solvothermal method was used to synthesize an MWCNT/MoS₂/BiVO₄ composite that showed perfect PEC properties because the MWCNTs and MoS₂/BiVO₄ heterostructures increased the composite’s stability against photocorrosion. Compared with the same signal of MWCNT/MoS₂/BiVO₄, the photocurrent signal of other composites was much smaller upon irradiation by visible light. According to this PEC sensor, the linear range of the H₂O₂ concentration was 1–200$\mu$M and 280–1560$\mu$M at $\mathrm{\text{pH}}=7.4$ based on the same pH when detecting ClO⁻ concentrations between 0.5–10$\mu$M and 20–340$\mu$M in a bleach sample. As a result, this sensor can be used to detect reactive oxygen species (ROS) in practical samples.

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.

In the modern world, several areas of our lives can be improved, in the form of diverse additional dimensions, in terms of quality, by machine learning. When building machine learning models, open data are often used. Although this trend is on the rise, the monetary losses since the attacks on machine learning models are also rising. Preparation is, thus, believed to be indispensable in terms of embarking upon machine learning. In this field of endeavor, machine learning models may be compromised in various ways, including poisoning attacks. Assaults of this nature involve the incorporation of injurious data into the training data rendering the models to be substantively less accurate. The circumstances of every individual case will determine the degree to which the impairment due to such intrusions can lead to extensive disruption. A modus operandi is proffered in this research as a safeguard for machine learning models in the face of the poisoning menace, envisaging a milieu in which machine learning models make use of data that emanate from numerous sources. The information in question will be presented as training data, and the diversity of sources will constitute a barrier to poisoning attacks in such circumstances. Every source is evaluated separately, with the weight of each data component assessed in terms of its ability to affect the precision of the machine learning model. An appraisal is also conducted on the basis of the theoretical effect of the use of corrupt data as from each source. The extent to which the subgroup of data in question can undermine overall accuracy depends on the estimated data removal rate associated with each of the sources described above. The exclusion of such isolated data based on this figure ensures that the standard data will not be tainted. To evaluate the efficacy of our suggested preventive measure, we evaluated it in comparison with the well-known standard techniques to assess the degree to which the model was providing accurate conclusions in the wake of the change. It was demonstrated during this test that when the innovative mode of appraisal was applied, in circumstances in which 17% of the training data are corrupt, the degree of precision offered by the model is 89%, in contrast to the figure of 83% acquired through the traditional technique. The corrective technique suggested by us thus boosted the resilience of the model against harmful intrusion.

Singlet oxygen (¹O₂) is a highly reactive oxygen species involved in numerous chemical and photochemical reactions in different biological systems and in particular, in photodynamic therapy (PDT). However, the quantification of ¹O₂ generation during in vitro and in vivo photosensitization is still technically challenging. To address this problem, indirect and direct methods for ¹O₂ detection have been intensively studied. This review presents the available methods currently in use or under development for detecting and quantifying ¹O₂ generation during photosensitization. The advantages and limitations of each method will be presented. Moreover, the future trends in developing PDT-¹O₂ dosimetry will be briefly discussed.

Leukemia is one of the ten types of cancer that causes the biggest death in the world. Compared to other types of cancer, leukemia has a low life expectancy, so an early diagnosis of the cancer is necessary. A new strategy has been developed to identify various leukemia biomarkers by making blood cancer biosensors, especially by developing nanomaterial applications so that they can improve the performance of the biosensor. Although many biosensors have been developed, the detection of leukemia by using nanomaterials with electrochemical and optical methods is still less carried out compare to other types of cancer biosensors. Even the acoustic and calorimetric testing methods for the detection of leukemia by utilizing nanomaterials have not yet been carried out. Most of the reviewed works reported the use of gold nanoparticles and electrochemical characterization methods for leukemia detection with the object of study being conventional cancer cells. In order to be used clinically by the community, future research must be carried out with a lot of patient blood objects, develop non-invasive leukemia detection, and be able to detect all types of blood cancer specifically with one biosensor. This can lead to a fast and accurate diagnosis thus allowing for early treatment and easy periodic condition monitoring for various types of leukemia based on its biomarker and future design controlable via internet of things (IoT) so that why would be monitoring real times.

Defect-rich single-walled carbon nanotubes (SWCNTs) were prepared by a water vapor flow-assisted chemical vapor deposition process. The correlation between defect density and water flow was quantitively studied using Raman spectrum. The detection capabilities of defective SWCNTs films toward perchlorate anions were investigated. It was found the defect-rich SWCNTs could adsorb more perchlorate anions owing to the strong chemical bonding between anions and defective sites. However, the detective response of defective SWCNTs toward perchlorate was not in compliance with anion adsorption. A tradeoff phenomenon between response and adsorption was found as the defect density of SWCNTs increased. This work is expected to provide a guidance to the future design of SWCNTs based ion detector.

Co₂P nanocomposites were successfully synthesized via a facile hydrothermal method. The microstructure, morphology and elemental composition were examined by XRD, SEM, TEM and XPS. The effects of synthesis temperature and reaction time on the sensing properties of Co₂P nanocomposites were analyzed. The Co₂P sensor at 200${}^{\circ }$C and 3 h reaction condition exhibited optimum sensitivity toward 100 ppm ethanol. In comparison with other gases, ethanol possesses good selective characteristics at optimal operating temperature of 160${}^{\circ }$C, which greatly reduce energy consumption. The above results showed that Co₂P nanocomposites have the potential application as an effective sensor for ethanol detection.

In this work, a novel probe 1 has been prepared via two-step reaction from o-phenylenediamine. The recognition properties of probe 1 for different metal ions in DMF solution were investigated by UV absorption spectroscopy and fluorescence spectroscopy, respectively. The color of the solution changed from light yellow to light purple, then became brown after half an hour when Ag${}^{+}$was added to the DMF solution of probe 1. Ag${}^{+}$ can also be easily and quickly identified by using the test strips prepared. 1 has also high selectivity and sensitivity for Ag${}^{+}$ even in the presence of other metal ions. After adding Ag${}^{+}$, the fluorescence quenching appeared and the lowest detection limit of Ag${}^{+}$ is 2.90 $\mu$M. Furthermore, the ¹H NMR test and density functional theory calculation have verified the sensing behavior mechanism. The detection of silver cation provided here is rapid and effective, and it is a beneficial exploration for the development of high efficiency ion sensors.

In this paper, ${\mathrm{\text{Cu(OH)}}}_2$ nanosheet arrays were directly grown in an upright manner on the surface of copper foam (CF) through a simple solvent reaction, followed by the successful reduction of ${\mathrm{\text{Cu(OH)}}}_2$ into ${\mathrm{\text{Cu}}}_2\mathrm{\text{O}}$ using hydrazine hydrate vapor, resulting in a binder-free ${\mathrm{\text{Cu}}}_2\mathrm{\text{O@CF}}$ array electrode. The in situ growth technique guarantees robust bonding between ${\mathrm{\text{Cu}}}_2\mathrm{\text{O}}$ and the conductive substrate, thereby enhancing electron transfer among various electrode components. Additionally, the vertically aligned array structures facilitate rapid penetration and transfer of the analyte, while also allowing for thorough exposure of the electroactive surfaces to the electrolyte. When utilized as an adhesive-free biosensor, the obtained ${\mathrm{\text{Cu}}}_2\mathrm{\text{O@CF}}$ array electrode exhibited sensitive catalytic oxidation activity toward acetaminophen (AP), providing a wide linear range of 2–200 $\mu$M and a low detection limit of 0.6 $\mu$M for AP detection. Furthermore, the developed ${\mathrm{\text{Cu}}}_2\mathrm{\text{O@CF}}$ array electrode was successfully utilized to detect AP in medicine samples. With its simple fabrication method, broad linear range, low detection limit, and excellent stability, this electrode holds significant promise for the detection of AP in pharmaceuticals.

In the last few decades, crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems. While human detection in partially crowded scenarios have achieved many reliable works, a highly dense crowd-like situation still is far from being solved. Densely crowded scenes offer patterns that could be used to tackle these challenges. This problem is challenging due to the crowd volume, occlusions, clutter and distortion. Crowd region classification is a precursor to several types of applications. In this paper, we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity. Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.

Cancer threatens the life and well-being of human beings. Millions of newly diagnosed cancer cases and a large number of deaths caused by cancer are reported each year in the world. Early detection and effective treatment are key to reduce cancer mortality, which can be potentially realized by using “theranostics”. Theranostics are a group of hybrid nanoparticles that perform in cancer patients to provide both diagnostic and therapeutic functions through a single nano-sized structure. In particular, core-shell structured theranostics have shown unique physicochemical properties, allowing them to facilitate molecular/cell targeting, bio-imaging, and drug delivery functions. This review, therefore, aims to present and discuss the recent development of research on core-shell structured theranostics. Specifically, it focuses on core-shell structured theranostics made of metals, silica and polymers. Different aspects, such as synthesis and structure, of core-shell structured theranostics are discussed in this review. This review helps readers to have a good understanding of the design and fabrication of core-shell structured theranostics.

The nonlinear interaction of electromagnetic radiation in microwave, terahertz, and optical regions with non-uniformly distributed space charge in the interelectrode space of vacuum devices is investigated. The detection of electromagnetic radiation in the vacuum electronic tubes (diode and triode) with parallel plate electrodes is experimentally demonstrated. The dependence of the detected signal on the incident radiation power, direction of wave polarization, current characteristics and frequency of modulating signal has been investigated.

The equation of motion of an electron in the field of electromagnetic wave in the presence of space charge was obtained, according to which, the detection is due to nonlinearity associated with the non-uniform distribution of electrons along the electrostatic field direction.

The measured detection characteristics are in reasonable agreement with theoretical estimates.

The purpose of this study is to identify and quantify the difference in detections of a subsurface target from a subsurface sensor source between range-independent and range-dependent versions of the same acoustic propagation model. Environmental data were pulled from open source databases to provide an application for the comparison and using novel measures of merit, the authors were able to quantify the difference in detection performance between models. This study suggests that provided multiple types of environments are considered, it is possible to use a range-independent model to give good approximations to the accuracy of detections, one would achieve using a full range-dependent sound propagation model.