ABSTRACT
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.
Subject(s)
COVID-19 , Humans , COVID-19/diagnosis , Electrodes , Microelectrodes , Polyesters , Printing, Three-Dimensional , BiomarkersABSTRACT
Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry;the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria: mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy: R2 > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8.
ABSTRACT
In the era of the coronavirus pandemic, one of the most demanding areas was the supply of healthcare systems in essential Personal Protection Equipment (PPE), including face-shields and hands-free door openers. This need, impossible to fill by traditional manufacturing methods, was met by implementing of such emerging technologies as additive manufacturing (AM/3D printing). In this article, Poly(lactic acid) (PLA) filaments for Fused filament fabrication (FFF) technology in the context of the antibacterial properties of finished products were analyzed. The methodology included 2D radiography and scanning electron microscopy (SEM) analysis to determine the presence of antimicrobial additives in the material and their impact on such hospital pathogens as Staphylococcus aureus, Pseudomonas aeruginosa, and Clostridium difficile. The results show that not all tested materials displayed the expected antimicrobial properties after processing in FFF technology. The results showed that in the case of specific species of bacteria, the FFF samples, produced using the declared antibacterial materials, may even stimulate the microbial growth. The novelty of the results relies on methodological approach exceeding scope of ISO 22196 standard and is based on tests with three different species of bacteria in two types of media simulating common body fluids that can be found on frequently touched, nosocomial surfaces. The data presented in this article is of pivotal meaning taking under consideration the increasing interest in application of such products in the clinical setting.
ABSTRACT
The motivation for research on 3D printing of protective face shields was the urgent societal demand for healthcare in the fight against the spread of COVID19 pandemic. Research is based on a literature review that shows that objects produced by additive technologies do not always have consistent quality suitable for the given purpose of use. Besides, they have different effects on the environment and leave different footprints. The overall goal of the research was to find out the most suitable thermoplastic material for printing shield frames in terms of mechanical properties, geometric accuracy, weight, printing time, filament price, and environmental sustainability. Fused deposition modeling (FDM) technology was used for 3D printing, and three different filaments were investigated: polylactic acid (PLA), polyethylene terephthalate (PETG), and polyhydroxyalkanoate (PHA). The weighted sum method for multi-objective optimization was used. Finally, PHA material was chosen, mainly due to its environmental sustainability, as it has the most negligible impact on the environment.
ABSTRACT
Three-dimensional (3D) printing offers the greatest potential to revolutionize the future of pharmaceutical manufacturing by overcoming challenges of conventional pharmaceutical operations and focusing design and production of dosage forms on the patient's needs. Of the many technologies available, fusion deposition modelling (FDM) is considered of the lowest cost and higher reproducibility and accessibility, offering clear advantages in drug delivery. FDM requires in-house production of filaments of drug-containing thermoplastic polymers by hot-melt extrusion (HME), and the prospect of connecting the two technologies has been under investigation. The ability to integrate HME and FDM and predict and tailor the filaments' properties will extend the range of printable polymers/formulations. Hence, this work revises the properties of the most common pharmaceutical-grade polymers used and their effect on extrudability, printability, and printing outcome, providing suitable processing windows for different raw materials. As a result, formulation selection will be more straightforward (considering the characteristics of drug and desired dosage form or release profile) and the processes setup will be more expedite (avoiding or mitigating typical processing issues), thus guaranteeing the success of both HME and FDM. Relevant techniques used to characterize filaments and 3D-printed dosage forms as an essential component for the evaluation of the quality output are also presented.