Carbon quantum dots are becoming powerful fluorophore materials for metal ion analysis. Here, highly passivated green phosphorous and nitrogen co-doped carbon quantum dots (C-dots) were prepared ...using low-temperature carbonization route. Strong green fluorescence emission around 490 nm and excitation wavelength independent C-dots were obtained. Morphological, surface, and optical properties of the C-dots were characterized. Fluorescence emission of C-dots was quenched selectively by copper ions and restored by adding copper chelators, such as EDTA and sulfide ions. Thus, C-dots were successfully used for direct determination of copper ions. Detection limit as low as 1.5 nM (s/
n
= 3) was achieved for copper ions. Such a low detection limit is very significant for metal analysis using our proposed facile method and low-cost substrates. Experimental results showed that the prepared C-dots demonstrated high sensitivity and selectivity for Cu
2+
ion detection and the method is robust and rugged.
Graphical abstract
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Abstract
Dual functional fluorescence nanosensors have many potential applications in biology and medicine. Monitoring temperature with higher precision at localized small length scales or in a ...nanocavity is a necessity in various applications. As well as the detection of biologically interesting metal ions using low-cost and sensitive approach is of great importance in bioanalysis. In this paper, we describe the preparation of dual-function highly fluorescent B, N-co-doped carbon nanodots (CDs) that work as chemical and thermal sensors. The CDs emit blue fluorescence peaked at 450 nm and exhibit up to 70% photoluminescence quantum yield with showing excitation-independent fluorescence. We also show that water-soluble CDs display temperature-dependent fluorescence and can serve as highly sensitive and reliable nanothermometers with a thermo-sensitivity 1.8% °C
−1
, and wide range thermo-sensing between 0–90 °C with excellent recovery. Moreover, the fluorescence emission of CDs are selectively quenched after the addition of Fe
2+
and Fe
3+
ions while show no quenching with adding other common metal cations and anions. The fluorescence emission shows a good linear correlation with concentration of Fe
2+
and Fe
3+
(R
2
= 0.9908 for Fe
2+
and R
2
= 0.9892 for Fe
3+
) with a detection limit of of 80.0 ± 0.5 nM for Fe
2+
and 110.0 ± 0.5 nM for Fe
3+
. Considering the high quantum yield and selectivity, CDs are exploited to design a nanoprobe towards iron detection in a biological sample. The fluorimetric assay is used to detect Fe
2+
in iron capsules and total iron in serum samples successfully.
Regarding porosity formation and gas content, choosing the appropriate shielding gas for laser welding is essential for achieving high-quality joints. Keyhole-induced porosity formation tendency and ...nitrogen content in SS 304 stainless steel welds were investigated based on the nitrogen content in shielding gases during fiber laser welding. Beads-on-plate autogenous welds were made at 5 kW continuous wave (CW) fiber laser in N
2
and Ar mixtures. Optical metallography, micro-focused X-ray, X-ray radiography, and high-speed images of the molten pool were used to investigate the porosity formation. In addition, a gas analyzer was used to study the weld metal nitrogen content. The results show that nitrogen significantly impacts the reduction of porosity in the melting zone and increases the dissolved nitrogen in the solidified weld metal, as using pure nitrogen leads to an increase in dissolved nitrogen by 36% higher than the nitrogen content in the base metal. In contrast, it has almost no significant effect on the keyhole mode.
Employing Deep Learning (DL) technologies to solve Civil Engineering problems is an emerging topic in recent years. However, due to the lack of labeled data, it is difficult to obtain accurate ...results with DL. One commonly used method to tackle this issue is to use affine transformation to augment the data set, but it can only generate new images that are highly correlated with the original ones. Moreover, unlike normal natural objects, distribution of structural images is much more complex and mixed. To address these challenges, Generative Adversarial Network (GAN) can be one feasible choice. We introduce one specific generative model, namely, Deep Convolutional Generative Adversarial Network (DCGAN) and propose a Leaf‐Bootstrapping (LB) method to improve the performance of this DCGAN. To effectively and quantitatively evaluate the quality of the synthetic images generated by DCGAN to complement human evaluation, Self‐Inception Score (SIS) and Generalization Ability (GA) are proposed. We also propose a pipeline based on Transfer Learning (TL) using synthetic images to help enhance a weak classifier performance under the condition of low‐data regime and limited computational resources. Finally, we conduct computer experiments with the proposed methods for two scenarios (scene level identification and damage state check) and one special synthetic data aggregation case. The results demonstrate the effectiveness and robustness of the proposed methods.
The increased use of digital tools such as smart phones, Internet of Things devices, cameras, and microphones, has led to the produuction of big data. Large data dimensionality, redundancy, and ...irrelevance are inherent challenging problems when it comes to big data. Feature selection is a necessary process to select the optimal subset of features when addressing such problems. In this paper, the authors propose a novel Binary Coronavirus Disease Optimization Algorithm (BCOVIDOA) for feature selection, where the Coronavirus Disease Optimization Algorithm (COVIDOA) is a new optimization technique that mimics the replication mechanism used by Coronavirus when hijacking human cells. The performance of the proposed algorithm is evaluated using twenty-six standard benchmark datasets from UCI Repository. The results are compared with nine recent wrapper feature selection algorithms. The experimental results demonstrate that the proposed BCOVIDOA significantly outperforms the existing algorithms in terms of accuracy, best cost, the average cost (AVG), standard deviation (STD), and size of selected features. Additionally, the Wilcoxon rank-sum test is calculated to prove the statistical significance of the results.
In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision‐based structural health monitoring (SHM). However, both data deficiency and class ...imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, oversampling, and undersampling, yet these ad hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one variant of the generative adversarial network (GAN), named the balanced semisupervised GAN (BSS‐GAN). It adopts the semisupervised learning concept and applies balanced‐batch sampling in training to resolve low‐data and imbalanced‐class problems. A series of computer experiments on concrete cracking and spalling classification were conducted under the low‐data imbalanced‐class regime with limited computing power. The results show that the BSS‐GAN is able to achieve better damage detection in terms of recall and Fβ score than other conventional methods, indicating its state‐of‐the‐art performance.
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•Studied eight (8) promising nanocarriers for anti-cancer drug delivery.•Studied up-to-date strategies for cancer site targeting used in SDDSs.•Various stimulus techniques utilized ...for drug release at targeted sites are mentioned.•Studied toxicity of various nanocarriers used in SDDSs.•Challenges and research scope of nanocarriers in cancer therapy also highlighted.
Nonspecific distribution and uncontrollable release of drugs in conventional drug delivery systems (CDDSs) have led to the development of smart nanocarrier-based drug delivery systems, which are also known as Smart Drug Delivery Systems (SDDSs). SDDSs can deliver drugs to the target sites with reduced dosage frequency and in a spatially controlled manner to mitigate the side effects experienced in CDDSs. Chemotherapy is widely used to treat cancer, which is the second leading cause of death worldwide. Site-specific drug delivery led to a keen interest in the SDDSs as an alternative to chemotherapy. Smart nanocarriers, nanoparticles used to carry drugs, are at the focus of SDDSs. A smart drug delivery system consists of smart nanocarriers, targeting mechanisms, and stimulus techniques. This review highlights the recent development of SDDSs for a number of smart nanocarriers, including liposomes, micelles, dendrimers, meso-porous silica nanoparticles, gold nanoparticles, super paramagnetic iron-oxide nanoparticles, carbon nanotubes, and quantum dots. The nanocarriers are described in terms of their structures, classification, synthesis and degree of smartness. Even though SDDSs feature a number of advantages over chemotherapy, there are major concerns about the toxicity of smart nanocarriers; therefore, a substantial study on the toxicity and biocompatibility of the nanocarriers has been reported. Finally, the challenges and future research scope in the field of SDDSs are also presented. It is expected that this review will be widely useful for those who have been seeking new research directions in this field and for those who are about to start their studies in smart nanocarrier-based drug delivery.
Highly sensitive non-contact mode temperature sensing is substantial for studying fundamental chemical reactions, biological processes, and applications in medical diagnostics. Nanoscale-based ...thermometers are guaranteeing non-invasive probes for sensitive and precise temperature sensing with subcellular resolution. Fluorescence-based temperature sensors have shown great capacity since they operate as “non-contact” mode and offer the dual functions of cellular imaging and sensing the temperature at the molecular level. Advancements in nanomaterials and nanotechnology have led to the development of novel sensors, such as nanothermometers (novel temperature-sensing materials with a high spatial resolution at the nanoscale). Such nanothermometers have been developed using different platforms such as fluorescent proteins, organic compounds, metal nanoparticles, rare-earth-doped nanoparticles, and semiconductor quantum dots. Carbon dots (CDs) have attracted interest in many research fields because of outstanding properties such as strong fluorescence, photobleaching resistance, chemical stability, low-cost precursors, low toxicity, and biocompatibility. Recent reports showed the thermal-sensing behavior of some CDs that make them an alternative to other nanomaterials-based thermometers. This kind of luminescent-based thermometer is promising for nanocavity temperature sensing and thermal mapping to grasp a better understanding of biological processes. With CDs still in its early stages as nanoscale-based material for thermal sensing, in this review, we provide a comprehensive understanding of this novel nanothermometer, methods of functionalization to enhance thermal sensitivity and resolution, and mechanism of the thermal sensing behavior.
Pollution haven hypothesis (PHH) assumes that polluting industries will move to regions with lesser rigorous environmental regulations. On the other hand, pollution halo hypothesis presumes that ...industries transfer their clean technologies through FDI inflows to the host countries. Following these theoretical perceptions, this paper empirically examines how foreign direct investment (FDI) affects pollution (CO
2
emissions) for four selected Asian countries (Malaysia, Philippines, Singapore, Thailand) over the period 1971-2014. This is done by applying the autoregressive distributed lags (ARDL) model of Pesaran et al.. The ARDL model is employed under two scenarios: without and with structural breaks. The long-run findings, for both scenarios, suggest support for the pollution haven hypothesis (PHH) for the Philippines only. Whereas, the findings lend support for the pollution halo hypothesis for Malaysia and Singapore. In addition, the paper explores the causality direction between FDI and pollution (CO
2
emissions) using the Vector Error Correction model (VECM). The results show mixed long- and short-run Granger causality findings.
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