Ratiometric intracellular pH sensors based on nitrogen-doped graphene oxide quantum dots

Advances and Opportunities of luminescence Nanomaterial for bioanalysis and diagnostics

Luminescence nanomaterials (LNMs) have recently received great attention in biological analysis and sensing owing to their key advances in easy design and functionalization with high photostability, luminescence stability, low autofluorescence, and multiphoton capacity. The number of publications surrounding LNMs for biological applications has grown rapidly. LNMs based on Stokes and anti-Stokes shifts are powerful tools for biological analysis. Especially, unique properties of anti-Stokes luminescence such as upconversion nanoparticles (UCNPs) with an implementation strategy to use longer-wavelength excitation sources such as near-infrared (NIR) light can depth penetrate to biological tissue for bioanalysis and bioimaging. We observed that the LNMs-based metal-organic frameworks (MOFs) have been developed and paid attention to the field of bioimaging and luminescence-based sensors, because of their structural flexibility, and multifunctionality for the encapsulation of luminophores. This article provides an overview of innovative LNMs such as quantum dots (QDs), UCNPs, and LMOFs. A brief summary of recent progress in design strategies and applications of LNMs including pH and temperature sensing in biologically responsive platforms, pathogen detection, molecular diagnosis, bioimaging, photodynamic, and radiation therapy published within the past three years is highlighted. It was found that the integrated nanosystem of lab-on-a-chip (LOC) with nanomaterials was rapidly widespread and erupting in interest after the COVID-19 pandemic. The simple operation and close processes of the integration nanosystem together with the optimized size and low energy and materials consumption of biochips and devices allow their trend study and application to develop portable and intelligent diagnostics tools. The last part of this work is the introduction of the utilization use of LNMs in LOC applications in terms of microfluidics and biodevices.

ResNet14Attention network for identifying the titration end-point of potassium dichromate

With the rapid development of industry, the increasing discharge of sewage causes the detection of water quality to be of increasing importance. Potassium dichromate titration is one of the most important testing methods in water quality detection; the ability to accurately identify the titration end-point of potassium dichromate is currently a research challenge. To identify titration end-point quickly and accurately, this study proposes a ResNet14Attention network, which utilizes residual modules that focus on original image information and an attention mechanism that focuses highly on classification targets. The proposed ResNet14Attention network is compared with 12 convolutional neural networks such as ResNet series networks, VGG, and GoogLeNet. The results of comparison experiments reveal that only the proposed ResNet14Attention network has the highest training and testing accuracy of 100% among all convolutional neural networks in the comparison experiment; the proposed ResNet14Attention network has the highest training speed compared to all the networks that over 90% accuracy.