As the scaling technology in the silicon-based semiconductor industry is approaching physical limits, it is necessary to search for proper materials to be utilized as alternatives for nanoscale devices and technologies. On the other hand, carbon-related nanomaterials have attracted so much attention from a vast variety of research and industry groups due to the outstanding electrical, optical, mechanical and thermal characteristics. Such materials have been used in a variety of devices in microelectronics. In particular, graphene and carbon nanotubes are extraordinarily favorable substances in the literature. Hence, investigation of carbon-related nanomaterials and nanostructures in different ranges of applications in science, technology and engineering is mandatory. This paper reviews the basics, advantages, drawbacks and investigates the recent progress and advances of such materials in micro and nanoelectronics, optoelectronics and biotechnology.

Particle separation and sorting techniques based on microfluidics have found extensive applications and are increasingly gaining prominence. This research presents the design and fabrication of a microfluidic device for separating cells using deterministic lateral displacement (DLD), enabling accuracy and continuity while being size-based. Nevertheless, it remains demanding, to completely reverse the detrimental effects of the boundaries that disturb the fluidic flow in the channel and reduce particle separation efficiency. This study introduces a novel approach to enhance the boundary structure of channels. By using this design, separation efficiency is boosted, and the fluid behavior around the walls is improved. The boundary correction (BC) enhances the operation of the microchannel and is very effective in microchannels. With boundary correction, the device exhibited improved separation efficiencies, but in its absence, separation efficiencies dropped. The collected microscopic images of the isolation of prostate cancer cell lines and red blood cells revealed promising outcomes. The efficiency of circulating tumor cell (CTC) throughput in the microfluidic channel, quantified as the ratio or proportion of tumor cells exiting the channel to cells entering it, exceeds 93%. Moreover, the efficiency of CTC isolation, expressed as the proportion of tumor cells from the upper outlet of the microfluidic channel to all cells, is over 89%. Additionally, the efficiency of red blood cell isolation, evaluated as the ratio of red blood cells from the lower outlet of the microfluidic channel to all cells, surpasses 77%. While using the same DLD separator without boundary correction reduced the separation efficiency by around 5%.

Smartphone-based colorimetric enzyme-linked immunosorbent assay (ELISA) readers have emerged as a cost-effective and portable alternative to conventional spectrophotometric systems, especially for use in resource-limited and point-of-care settings. In our previous work, we developed a digital image colorimetry platform integrated with a 3D-printed optomechanical system and smartphone imaging, achieving high diagnostic accuracy for cancer cell lines through linear regression modeling of red, green, and blue (RGB) intensities. While effective, this linear approach could not fully capture the nonlinear relationships intrinsic to enzymatic colorimetric reactions and light–matter interactions. In this study, we present a significant enhancement to our prior model by implementing a nonlinear machine learning framework, eXtreme gradient boosting (XGBoost), to better predict optical densities from smartphone-captured RGB data. Utilizing the same hardware platform and experimental protocol as our earlier system, we extracted RGB values from ELISA images under RGB backlighting and then expanded the feature space through engineered transformations. These features were used to train and validate the XGBoost model on multiple human cancer cell lines (HE4, PC3, 5637, and ACHN). Our XGBoost model achieved exceptional predictive performance with R ² > 0.999 across all tested cell lines, substantially improving upon the linear regression model (R ² range: 0.923–0.996). Root mean square error was reduced by over 98%, and Spearman and Pearson correlation coefficients approached unity, demonstrating excellent trend fidelity and linear alignment with FDA-certified ELISA readers (Epoch and Tecan). The model’s robustness was further confirmed through k-fold cross-validation and rigorous statistical analysis. This advancement positions our platform as a powerful candidate for decentralized diagnostics and high-throughput screening, capable of operating reliably across different smartphones and lighting conditions.