A versatile 3D printed multi-electrode cell for determination of three COVID-19 biomarkers.

3D-printing has shown an outstanding performance for the production of versatile electrochemical devices. However, there is a lack of studies in the field of 3D-printed miniaturized settings for multiplex biosensing. In this work, we propose a fully 3D-printed micro-volume cell containing six working electrodes (WEs) that operates with 250 µL of sample. A polylactic acid/carbon black conductive filament (PLA/CB) was used to print the WEs and subsequently modified with graphene oxide (GO), to support protein binding. Cyclic voltammetry was employed to investigate the electrochemical behaviour of the novel multi-electrode cell. In the presence of K3[Fe(CN)6], PLA/CB/GO showed adequate peak resolution for subsequent label-free immunosensing. The innovative 3D-printed cell was applied for multiplex voltammetric detection of three COVID-19 biomarkers as a proof-of-concept. The multiple sensors showed a wide linear range with detection limits of 5, 1 and 1 pg mL-1 for N-protein, SRBD-protein, and anti-SRBD, respectively. The sensor performance enabled the selective sequential detection of N protein, SRBD protein, and anti-SRBD at biological levels in saliva and serum. In summary, the miniaturized six-electrode cell presents an alternative for the low-cost and fast production of customizable devices for multi-target sensing with promising application in the development of point-of-care sensors.

Improving selective channel occlusion of complex hydrocarbons and fatty acid methyl esters in urea crystals by using an expendable 3D-printed microfluidic device for sample preparation in untargeted petroleomics.

In this study, we describe a proof-of-concept investigation of the potential and limitations of employing channel occlusion for sample preparation in untargeted analysis in petroleomics. A middle petroleum distillate composed of fatty acid methyl esters (FAME) and a complex mixture of linear, branched, and cyclic hydrocarbons were selected as the model samples for this investigation. A microfluidic device was engineered to overcome the limitations of channel occlusion, resulting in a quick and robust method for sample preparation. The 3D-printed device using fused deposition modelling (FDM) allowed the combination of a 13-h multi-step sample handling protocol into a 2-min single-step procedure, which is also automation-friendly. Such developments were also evaluated using the analytical eco-scale to guide the development of a green analytical method. The relative standard deviation decreased 2-fold with method miniaturization. The efficiency of n-alkane removal was extended from tridecane (n-C13) to heptadecane (n-C17), compared to original method (n-C16 to n-C17). The analytical performance of the method was investigated for untargeted analysis. The tool used to probe the intra- and inter-class variance was multi-way principal component analysis (MPCA). MPCA modelling revealed that both methods generated equivalent chemical information, highlighting the benefits of reliable and reproducible sample preparation methods, especially for untargeted analysis. Such awareness is critical to avoid the generation of misleading results in fields that heavily rely on untargeted analysis and fingerprinting, such as petroleomics.