3D-Printed Lab-on-a-Chip Diagnostic Systems-Developing a Safe-by-Design Manufacturing Approach

Karayannis, Panagiotis; Petrakli, Foteini; Gkika, Anastasia; Koumoulos, Elias P.

doi:10.3390/mi10120825

Cited by 16 publications

(4 citation statements)

References 90 publications

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“…Engineering the LOC devices enables the control of microfluidics, which is key for the electrokinetic or micropumping control of fluidic transportation and efficient separation when examining liquid samples with high precision in several conditions; when the flow is continuous, or droplet-wise sampled. Thus, this is an important technology for biomedicine advancement following the automatic continuous tracking, which can be used for online feedback and adjustment of multi-material AM processes and provide tailor-made microfluidic micro-electromechanical systems (MEMS) [ 85 ].…”

Section: In Silico Materials Developmentmentioning

confidence: 99%

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Konstantopoulos

Koumoulos²,

Charitidis

2022

Nanomaterials

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Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

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Section: In Silico Materials Developmentmentioning

confidence: 99%

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Konstantopoulos

Koumoulos²,

Charitidis

2022

Nanomaterials

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“…3D printing assisted microfluidic device fabrication is one of the most sorted out area of interest when it comes to analytical device development, providing efficient solutions to handle measurements in a miniaturized chip [1]. Conventionally, 3 Ankit Kumar and Prathul Nath contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

A 3D printing assisted microfluidic absorbance-based measurement system for biological assay

Kumar,

Nath,

Das

et al. 2024

Meas. Sci. Technol.

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Development of rapid analytical systems utilizing 3D printing is an emerging area of interest with the potential to provide efficient solutions by integrating multidisciplinary technology without compromising the quality of the system. In this study we report the fabrication of a 3D printed assisted microfluidic based absorbance measurement system, leveraging 3D printing along with integrating miniature optical components for the accurate measurement of biological assays. The developed system is rapid, affordable and compact, through set of computer-aided design (CAD) models and Fusion Deposition Modeling (FDM) 3D printing along with relevant electronic circuitry involving optical components like Surface Mounting Devices (SMD). The handheld device features a capacitive touchscreen display, programmed to seamlessly perform MTT assay. The device was employed for assessing the cell viability using MCF-7 cell lines over varying concentrations of Tamoxifen, reciprocating the MTT assay analysis conducted by using spectrometer. The device achieved excellent results which upon comparison with the conventional spectrometer-based results have shown a correlation coefficient of 0.98. This compact and rapid absorbance measurement system holds significant potential for evaluating the cytotoxicity of drugs, and further development of innovative analytical devices.

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“…2 Among them, ABS and PLA are the most commonly used filaments in 3D printers and are fabricated at a nozzle temperature of 220 and 180 °C, respectively. 3 At this temperature, the thermal processing takes place and ultrafine particles (UFP) and volatile organic compounds are released from these thermoplastics. 4 A significant difference has been reported for the UFP released per min by PLA (∼2.0 × 10 10 # min −1 ) and ABS (∼1.9 × 10 11 # min −1 ) during additive manufacturing.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Several fused filaments are widely used for 3D printing like acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polyethylene terephthalate (PET) or polyethylene terephthalate glycol-modified (PETG), nylon polyamide, high-impact polystyrene (HIPS), poly(vinyl alcohol) (PVA), polypropylene (PP), polyphenylsulfone (PPSF or PPSU), other polymers, metal, wood or ceramic materials . Among them, ABS and PLA are the most commonly used filaments in 3D printers and are fabricated at a nozzle temperature of 220 and 180 °C, respectively . At this temperature, the thermal processing takes place and ultrafine particles (UFP) and volatile organic compounds are released from these thermoplastics .…”

Section: Introductionmentioning

confidence: 99%

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene

Pandit

Singh

Sinha

et al. 2021

Chem. Res. Toxicol.

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Additive manufacturing commonly known as 3D printing has numerous applications in several domains including material and biomedical technologies and has emerged as a tool of capabilities by providing fast, highly customized, and cost-effective solutions. However, the impact of the printing materials and chemicals present in the printing fumes has raised concerns about their adverse potential affecting humans and the environment. Thus, it is necessary to understand the properties of the chemicals emitted during additive manufacturing for developing safe and biocompatible fibers having controlled emission of fumes including its sustainable usage. Therefore, in this study, we have developed a computational predictive risk-assessment framework on the comprehensive list of chemicals released during 3D printing using the acrylonitrile butadiene styrene (ABS) filament. Our results showed that the chemicals present in the fumes of the ABS-based fiber used in additive manufacturing have the potential to lead to various toxicity end points such as inhalation toxicity, oral toxicity, carcinogenicity, hepatotoxicity, and teratogenicity. Moreover, because of their absorption, distribution in the body, metabolism, and excretion properties, most of the chemicals exhibited a high absorption level in the intestine and the potential to cross the blood-brain barrier. Furthermore, pathway analysis revealed that signaling like alpha-adrenergic receptor signaling, heterotrimeric G-protein signaling, and Alzheimer's disease-amyloid secretase pathway are significantly overrepresented given the identified target proteins of these chemicals. These findings signify the adversities associated with 3D printing fumes and the necessity for the development of biodegradable and considerably safer fibers for 3D printing technology.

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3D-Printed Lab-on-a-Chip Diagnostic Systems-Developing a Safe-by-Design Manufacturing Approach

Cited by 16 publications

References 90 publications

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

A 3D printing assisted microfluidic absorbance-based measurement system for biological assay

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene

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