Application of digital twins for simulation based tailoring of laser induced graphene.

In the era of man-machine interfaces, digital twins stand as a key technology, offering virtual representations of real-world objects, processes, and systems through computational models. They enable novel ways of interacting with, comprehending, and manipulating real-world entities within a virtual realm. The real implementation of graphene-based sensors and electronic devices remains challenging due to the integration complexities of high-quality graphene materials with existing manufacturing processes. To address this, scalable techniques for the in-situ fabrication of graphene-like materials are essential. One promising method involves using a CO2 laser to convert polyimide into graphene. Optimizing this graphitization process is hindered by complex parameter interactions and nonlinear terms. This article explores how these digital replicas can enhance the fabrication of laser-induced graphene (LIG) through laser simulation and machine learning methods to enable rapid single-step LIG patterning. This approach aims to create a universal simulation for all CO2 lasers, calculating optical energy flux and utilizing machine learning to control and predict LIG conductivity (ability to conduct current), morphology, and electrical resistance. The proposed procedure, integrating digital twins in the LIG production process, will avoid or reduce the preliminary tests required to determine the proper laser parameters to reach the desired LIG characteristics. Accordingly, this approach will reduce the time and costs associated with these tests and thus increase the efficiency and optimize the procedure.

Detection of COVID-19: A Smartphone-Based Machine-Learning-Assisted ECL Immunoassay Approach with the Ability of RT-PCR CT Value Prediction.

The unstoppable spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has severely threatened public health over the past 2 years. The current ubiquitously accepted method for its diagnosis provides sensitive detection of the virus; however, it is relatively time-consuming and costly, not to mention the need for highly skilled personnel. There is a clear need to develop novel computer-based diagnostic tools to provide rapid, cost-efficient, and time-saving detection in places where massive traditional testing is not practical. Here, we develop an electrochemiluminescence (ECL)-based detection system whose results are quantified as reverse transcriptase polymerase chain reaction (RT-PCR) cyclic threshold (CT) values. A concentration-dependent signal is generated upon the introduction of the virus to the electrode and is recorded with a smartphone camera. The ECL images are used to train machine learning algorithms, and a model using artificial neural networks (ANNs) for 45 samples was developed. The model demonstrated more than 90% accuracy in the diagnosis of 50 unknown real samples, detecting up to a CT value of 32 and a limit of detection (LOD) of 10-12 g mL-1 in the testing of artificial samples.

Recent trends and advancements in electrochemiluminescence biosensors for human virus detection.

Researchers are constantly looking to find new techniques of virus detection that are sensitive, cost-effective, and accurate. Additionally, they can be used as a point-of-care (POC) tool due to the fact that the populace is growing at a quick tempo, and epidemics are materializing greater often than ever. Electrochemiluminescence-based (ECL) biosensors for the detection of viruses have become one of the most quickly developing sensors in this field. Thus, we here focus on recent trends and developments of these sensors with regard to virus detection. Also, quantitative analysis of various viruses (e.g., Influenza virus, SARS-CoV-2, HIV, HPV, Hepatitis virus, and Zika virus) with a specific interest in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was introduced from the perspective of the biomarker and the biological receptor immobilized on the ECL-based sensors, such as nucleic acids-based, immunosensors, and other affinity ECL biosensors.

Development of sandwich electrochemiluminescence immunosensor for COVID-19 diagnosis by SARS-CoV-2 spike protein detection based on Au@BSA-luminol nanocomposites.

Coronavirus disease (COVID-19) is a new and highly contagious disease posing a threat to global public health and wreaking havoc around the world. It's caused by the Coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2). In the current pandemic situation, rapid and accurate SARS-CoV-2 diagnosis on a large scale is critical for early-stage diagnosis. Early detection and monitoring of viral infections can aid in controlling and preventing infection in large groups of people. Accordingly, we developed a sensitive and high-throughput sandwich electrochemiluminescence immunosensor based on antigen detection for COVID-19 diagnosis (the spike protein of SARS-CoV-2). For the spike protein of SARS-CoV-2, the ECL biosensor had a linear range of 10 ng mL-1 to 10 µg mL-1 with a limit of detection of 1.93 ng mL-1. The sandwich ECL immunosensor could be used in early clinical diagnosis due to its excellent recovery in detecting SARS-CoV-2, rapid analysis (90 min), and ease of use.

Multiplex Detection of Antibiotic Residues in Milk: Application of MCR-ALS on Excitation-Emission Matrix Fluorescence (EEMF) Data Sets.

The presence of antibiotics and their metabolites in milk and dairy products is a serious concern because of their harmful effects on human health. In the current study, a novel synergistic bimetallic nanocluster with gold and silver as an emission fluorescence probe was investigated for the simultaneous determination of tetracycline (TC), ampicillin (AMP), and sulfacetamide (SAC) antibiotics in the milk samples using excitation-emission matrix fluorescence (EEMF) spectroscopy. The multivariate curve resolution-alternating least squares (MCR-ALS) method was implemented to analyze augmented EEMF data sets to quantify the multicomponent systems in the presence of interferences with considerable spectral overlap. A pseudo-univariate calibration curve of the resolved emission spectra intensity against the concentration of the mentioned antibiotics was linear in the range of 5-5000 ng mL-1 for AMP and 50-5000 ng mL-1 for TC and SAC. The calculated values of the limit of detection ranged between 1.4 and 14.6 ng mL-1 with a relative standard deviation (RSD) of less than 4.9%. The obtained results show that the EEMF/MCR-ALS methodology using an emission fluorescence probe is a powerful tool for the simultaneous quantification of TC, AMP, and SAC in complex matrices with highly overlapped spectra.

Sensitive colorimetric aptasensor based on g-C3N4@Cu2O composites for detection of Salmonella typhimurium in food and water.

A new colorimetric aptasensor equipped with a novel composite of graphitic carbon nitride (g-C3N4) nanosheets and copper oxide(I) (Cu2O) nanocrystals is presented for Salmonella typhimurium (S .typhimurium). The dual-purpose structure of this composite simultaneously contributes to superb peroxidase-like activity and interaction with a label-free aptamer. Although g-C3N4@Cu2O effectively creates a visible blue color following the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) in presence of hydrogen peroxide (H2O2), this catalytic activity of the composite severely decreases after the interaction with aptamers. In the presence of S. typhimurium in sample, aptamers bound to their specific target. Subsequently, g-C3N4@Cu2O catalytic activity was enhanced in proportion to S. typhimurium concentration. Under optimized conditions, this aptasensor exhibited an excellent detection performance in a range from 1.5 × 101 to 1.5 × 105 CFU/ml, with a detection limit of 15 CFU/ml. Besides, portable detection of S. typhimurium by paper-based model of this method operated successfully in just 6 min. Analysis of spiked milk samples revealed high potential of this method as a sensitive, rapid, and label-free promising tool for S. typhimurium detection. A novel composite of g-C3N4 nanosheets and Cu2O nanocrystals was constructed through this study, which represented a collection of significant properties for designing an aptasensor. The simultaneous capability of this composite for peroxidase-like activity and interaction with aptamer led to design a fast accurate biosensor for detecting as low as 15 CFU/ml Salmonella typhimurium as one of the most important foodborne pathogens which is a persistent burden for societies.

A highly sensitive fluorescent immunosensor for sensitive detection of nuclear matrix protein 22 as biomarker for early stage diagnosis of bladder cancer.

A novel strategy is reported for highly sensitive, rapid, and selective detection of nuclear matrix protein NMP22 using two-color quantum dots based on fluorescence resonance energy transfer (FRET). Quantum dots (QDs) are highly advantageous for biological imaging and analysis, particularly when combined with (FRET) properties of semiconductor quantum dot (QDs) are ideal for biological analysis to improve sensitivity and accuracy. In this FRET system narrowly dispersed green emitting quantum dot CdTe core is used as a donor and labelled by monoclonal (mAb) antibody, while orange emitting quantum dot CdTe/CdS core shell is used as an accepter and labelled by polyclonal (pAb) antibody. The quantum dots are labelled by antibodies using EDC/NHS as crosslinking agent. Bovine serum albumin (BSA) solution was added to block nonspecific binding sites. The fluorescence intensity of QDs accepter decreased linearly with the increasing concentrations of NMP22 from 2-22 pg mL-1 due to FRET system and fluoroimmunoassay reaction. This method has good regression coefficient (R 2 = 0.998) and detection limit was 0.05 pg mL-1. The proposed FRET-based immunosensor provides a quick, simple and sensitive immunoassay tool for protein detection, and can be considered as a promising approach for clinical applications. The proposed FRET-based immunosensor provides a quick, simple and sensitive immunoassay tool for protein detection, and can be considered as a promising approach for clinical applications.

Enhanced peroxidase-like activity of platinum nanoparticles decorated on nickel- and nitrogen-doped graphene nanotubes: colorimetric detection of glucose.

A nanostructured catalyst is introduced that demonstrates peroxidase mimicking activity. It consists of nickel- and nitrogen-doped graphene nanotubes loaded with platinum nanoparticles. Pt-decorated Ni-doped nitrogen-rich graphitic nanotube (Pt/Ni@NGT) was synthesized using a two-step procedure in which the precursors were first refluxed to form a supramolecular assembly followed by a pyrolysis and leaching step to form nanotubes. Afterwards, Pt was decorated on the outer surface of nanotube by an ultrasound assisted method. Pt/Ni@NGT was characterized by XPS, TEM, SEM, and HAADF-STEM. The as-prepared Pt/Ni@NGT nanostructure was used for the detection of glucose via catalyzing the oxidation of a substrate, 3,3',5,5'-tetramethylbenzidine (TMB), to form a blue product (ox-TMB), thereby enabling colorimetric assay for enzymatically generated H2O2. The nanostructure exhibited excellent biocompatibility and led to highly efficient immobilization and retention of GOx. The method has a linear response in the 43 pM to 220 µM glucose concentration range, a detection limit as low as 1 pM and a limit of quantification of 3.4pM, along with good reproducibility(< 3%). A paper based visual microfluidic assay was also worked out that has an analytical range that extends from 0.1-50 mM. It is simple and rapid enough to be useful as a glucose home test.. The method was successfully applied to the determination of glucose in tear and saliva samples. Graphical abstract Graphene nanotubes doped with nitrogen and nickel (Ni@NGT) have been synthesized as the support to construct the unique Pt/Ni@NGT for providing artificial peroxidase activity for the GOx-based detection of glucose, which was further used for the construction of a glucose paper assay.

Cerium(III) Ion Sensing Based on Graphene Quantum Dots Fluorescent Turn-Off.

In this work, graphene quantum dots (GQDs) was synthesized through hydrothermal method and used as a photoluminescent bulk nano-chemosensor for detection of Ce3+ ion in the aqueous solution. The synthesized GQD was characterized by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), UV-Visible absorption and fluorescence emission spectroscopy. The sheet diameters of the synthesized GQDs were mainly distributed in the range of 15-20 nm. The interactions of GQDs with common cations and lanthanide ions were studied by fluorescence spectroscopy. Among the tested cations, Ce3+ ions was able to quench the fluorescence emission intensity of the GQD selectively. This quenching can be attributed to a redox mechanism between Ce3+ ion on the GQDs surface.