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Luminescence lifetimes are an attractive analytical method for detection due to its high sensitivity and stability. Iridium probes exhibit luminescence with long excited-state lifetimes, which are sensitive to the local environment. Perfluorooctanoic acid (PFOA) is listed as a chemical of high concern regarding its toxicity and is classified as a “forever chemical”. In addition to strict limits on the presence of PFOA in drinking water, environmental contamination from industrial effluent or chemical spills requires rapid, simple, accurate, and cost-effective analysis in order to aid containment. Herein, we report the fabrication and function of a novel and facile luminescence sensor for PFOA based on iridium modified on gold surfaces. These surfaces were modified with lipophilic iridium complexes bearing alkyl chains, namely, IrC6 and IrC12, and Zonyl-FSA surfactant. Upon addition of PFOA, the modified surfaces IrC6-FSA@Au and IrC12-FSA @Au show the largest change in the red luminescence signal with changes in the luminescence lifetime that allow monitoring of PFOA concentrations in aqueous solutions. The platform was tested for the measurement of PFOA in aqueous samples spiked with known concentrations of PFOA and demonstrated the capacity to determine PFOA at concentrations >100 μg/L (240 nM).
Functionalized Ti3C2Tx nanosheets based biosensor for point-of-care detection of SARS-CoV‑2 antigen
(2023)
MXenes are considered a promising class of two-dimensional materials with extraordinary physical and electrochemical properties. Distinguished features like high specific surface area and outstanding electrical conductivity make them suitable for electrochemical biosensing applications. Here, we report the development of a biosensor involving the functionalized MXene−titanium carbide nanosheets (Ti3C2Tx-NS) and monoclonal antibodies against the SARS-CoV-2 nucleocapsid protein (anti-SARS-CoV-2 mAb) to design a point-of-care device for detection of the SARS-CoV-2 nucleocapsid protein (SARS-CoV-2 NP) antigen. Few-layered titanium carbide nanosheets (denoted as FL-Ti3C2Tx-NS) have been synthesized using a single-step etching and delamination method and characterized using optical and electron microscopy techniques revealing the suitability for immunosensing applications. Binding studies revealed the excellent affinity between the biosensor and the SARS-CoV-2 NP. Electrochemical detection of SARS-CoV-2 NP is performed using differential pulse voltammetry and read by a smartphone-based user interface. The proposed FL-Ti3C2Tx-NS based biosensor offers the detection of SARS-CoV-2 NP with a limit of detection of 0.91 nM in a wide detection range in spiked saliva samples. Additionally, there is no cross-reactivity in the presence of potential interferants like SARS-CoV-2 spike glycoprotein and bovine serum albumin. These findings demonstrate the potential of MXenes in developing a rapid and reliable tool for SARS-CoV-2 NP detection. While we report the biosensing of SARS-CoV-2 NP, our system also paves the way for the detection of other SARS-CoV-2 antigens like spike protein or other biomolecules based on antigen−antibody interactions.
In the present talk the basics of the Raman spectroscopy and particularly of Raman microscopy are explained. Advantages and disadvantages of the method are highlighted through selected case studies. In the second part of the lecture examples of correlative imaging with electron, X-ray, ion and optical microscopies from micro- to the nanoscale are highlighted.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6):
B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6
It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.
Electrochemical methods offer great promise in meeting the demand for user-friendly on-site devices for Monitoring important parameters. The food industry often runs own lab procedures, for example, for mycotoxin analysis, but it is a major goal to simplify analysis, linking analytical methods with smart technologies. Enzyme-linked immunosorbent assays, with photometric detection of 3,3’,5,5’-tetramethylbenzidine (TMB),form a good basis for sensitive detection. To provide a straightforward approach for the miniaturization of the detectionstep, we have studied the pitfalls of the electrochemical TMB detection. By cyclic voltammetry it was found that the TMB electrochemistry is strongly dependent on the pH and the electrode material. A stable electrode response to TMB could be achieved at pH 1 on gold electrodes. We created a smartphonebased, electrochemical, immunomagnetic assay for the detection of ochratoxin A in real samples, providing a solid basis forsensing of further analytes.
Cost-effective water cleaning approaches using improved Treatment technologies, for instance based on catalytic processes with high activity catalysts, are urgently needed. The aim of our study was to synthesize efficient Fenton-like photo-catalysts for rapid degradation of persistent organic micropollutants in aqueous medium. Iron-based nanomaterials were chemically synthesized through simple procedures by immobilization of either iron(II) oxalate (FeO) or iron(III) citrate (FeC) on magnetite (M) nanoparticles stabilized with polyethylene glycol (PEG). Various investigation techniques were performed in order to characterize the freshly prepared catalysts. By applying advanced oxidation processes, the effect of catalyst dosage, hydrogen peroxide concentration and UV-A light exposure were examined for Bisphenol A (BPA) conversion, at laboratory scale, in mild conditions. The obtained results revealed that BPA degradation was rapidly enhanced in the presence of low-concentration H2O2, as well as under UV-A light, and is highly dependent on the surface characteristics of the catalyst. Complete photo-degradation of BPA was achieved over the M/PEG/FeO catalyst in less than 15 minutes. Based on the catalytic performance, a hierarchy of the tested catalysts was established: M/PEG/FeO > M/PEG/FeC > M/PEG. The results of cytotoxicity assay using MCF-7 cells indicated that the aqueous samples after treatment are less cytotoxic.