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

A review on toxicity mechanism and risk factors of nanoparticles in respiratory tract

This comprehensive review focuses on various dimensions of nanoparticle toxicity, emphasizing toxicological characteristics, assessment techniques, and examinations of relevant studies on the effects on biological systems. The primary objective is to comprehend the potential risks associated with nanoparticles and to provide efficient strategies for mitigating them by consolidating current research discoveries. For in-depth insights, the discussions extend to crucial aspects such as toxicity associated with different nanoparticles, human exposure, and nanoparticle deposition in the human respiratory tract. The analysis utilizes the multiple-path particle dosimetry (MPPD) modeling for computational simulation. The SiO2 nanoparticles with a volume concentration of 1% and a particle size of 50 nm are used to depict the MPPD modeling of the Left upper (LU), left lower (LL), right upper (RU), right middle (RM), and right lower (RL) lobes in the respiratory tract. The analysis revealed a substantial 67.5% decrease in the deposition fraction as the particle size increased from 10 nm to 100 nm. Graphical representation emphasizes the significant impact of exposure path selection on nanoparticle deposition, with distinct deposition values observed for nasal, oral, oronasal-mouth breather, oronasal - normal augmenter, and endotracheal paths (0.00291 μg, 0.00332 μg, 0.00297 μg, 0.00291 μg, and 0.00383 μg, respectively). Consistent with the focus of the review, the article also addresses crucial mitigation strategies for managing nanoparticle toxicity.

Learning from Safe-by-Design for Safe-and-Sustainable-by-Design: Mapping the current landscape of Safe-by-Design reviews, case studies, and frameworks

With the introduction of the European Commission's "Safe and Sustainable-by-Design" (SSbD) framework, the interest in understanding the implications of safety and sustainability assessments of chemicals, materials, and processes at early-innovation stages has skyrocketed. Our study focuses on the "Safe-by-Design" (SbD) approach from the nanomaterials sector, which predates the SSbD framework. In this assessment, SbD studies have been compiled and categorized into reviews, case studies, and frameworks. Reviews of SbD tools have been further classified as quantitative, qualitative, or toolboxes and repositories. We assessed the SbD case studies and classified them into three categories: safe(r)-by-modeling, safe(r)-by-selection, or safe(r)-by-redesign. This classification enabled us to understand past SbD work and subsequently use it to define future SSbD work so as to avoid confusion and possibilities of "SSbD-washing" (similar to greenwashing). Finally, the preexisting SbD frameworks have been studied and contextualized against the SSbD framework. Several key recommendations for SSbD based on our analysis can be made. Knowledge gained from existing approaches such as SbD, green and sustainable chemistry, and benign-by-design approaches needs to be preserved and effectively transferred to SSbD. Better incorporation of chemical and material functionality into the SSbD framework is required. The concept of lifecycle thinking and the stage-gate innovation model need to be reconciled for SSbD. The development of high-throughput screening models is critical for the operationalization of SSbD. We conclude that the rapid pace of both SbD and SSbD development necessitates a regular mapping of the newly published literature that is relevant to this field.

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

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.

Identification of effective control technologies for additive manufacturing

Additive manufacturing (AM) refers to several types of processes that join materials to build objects, often layer-by-layer, from a computer-aided design file. Many AM processes release potentially hazardous particles and gases during printing and associated tasks. There is limited understanding of the efficacy of controls including elimination, substitution, administrative, and personal protective technologies to reduce or remove emissions, which is an impediment to implementation of risk mitigation strategies. The Medline, Embase, Environmental Science Collection, CINAHL, Scopus, and Web of Science databases and other resources were used to identify 42 articles that met the inclusion criteria for this review. Key findings were as follows: 1) engineering controls for material extrusion-type fused filament fabrication (FFF) 3-D printers and material jetting printers that included local exhaust ventilation generally exhibited higher efficacy to decrease particle and gas levels compared with isolation alone, and 2) engineering controls for particle emissions from FFF 3-D printers displayed higher efficacy for ultrafine particles compared with fine particles and in test chambers compared with real-world settings. Critical knowledge gaps identified included a need for data: 1) on efficacy of controls for all AM process types, 2) better understanding approaches to control particles over a range of sizes and gas-phase emissions, 3) obtained using a standardized collection approach to facilitate inter-comparison of study results, 4) approaches that go beyond the inhalation exposure pathway to include controls to minimize dermal exposures, and 5) to evaluate not just the engineering tier, but also the prevention-through-design and other tiers of the hierarchy of controls.

Fused Filament Fabrication 3D Printing: Quantification of Exposure to Airborne Particles

Fused Filament Fabrication (FFF) has been established as a widely practiced Additive Manufacturing technique, using various thermoplastic filaments. Carbon fibre (CF) additives enhance mechanical properties of the materials. The main operational hazard of the FFF technique explored in the literature is the emission of Ultrafine Particles and Volatile Organic Compounds. Exposure data regarding novel materials and larger scale operations is, however, still lacking. In this work, a thorough exposure assessment measurement campaign is presented for a workplace applying FFF 3D printing in various setups (four different commercial devices, including a modified commercial printer) and applying various materials (polylactic acid, thermoplastic polyurethane, copolyamide, polyethylene terephthalate glycol) and CF-reinforced thermoplastics (thermoplastic polyurethane, polylactic acid, polyamide). Portable exposure assessment instruments are employed, based on an established methodology, to study the airborne particle exposure potential of each process setup. The results revealed a distinct exposure profile for each process, necessitating a different safety approach per setup. Crucially, high potential for exposure is detected in processes with two printers working simultaneously. An updated engineering control scheme is applied to control exposures for the modified commercial printer. The establishment of a flexible safety system is vital for workplaces that apply FFF 3D printing.

Facilitating Safe FFF 3D Printing: A Prototype Material Case Study

Three-dimensional (3D) printing has introduced a paradigm shift in the manufacturing world, and it is increasing in popularity. In cases of such rapid and widespread acceptance of novel technologies, material or process safety issues may be underestimated, due to safety research being outpaced by the breakthroughs of innovation. However, a definitive approach in studying the various occupational or environmental risks of new technologies is a vital part of their sustainable application. In fused filament fabrication (FFF) 3D printing, the practicality and simplicity of the method are juxtaposed by ultrafine particle (UFP) and volatile organic compound (VOC) emission hazards. In this work, the decision of selecting the optimal material for the mass production of a microfluidic device substrate via FFF 3D printing is supported by an emission/exposure assessment. Three candidate prototype materials are evaluated in terms of their comparative emission potential. The impact of nozzle temperature settings, as well as the microfluidic device’s structural characteristics regarding the magnitude of emissions, is evaluated. The projected exposure of the employees operating the 3D printer is determined. The concept behind this series of experiments is proposed as a methodology to generate an additional set of decision-support decision-making criteria for FFF 3D printing production cases.

State of the art in additive manufacturing and its possible chemical and particle hazards-review

Additive manufacturing, enabling rapid prototyping and so-called on-demand production, has become a common method of creating parts or whole devices. On a 3D printer, real objects are produced layer by layer, thus creating extraordinary possibilities as to the number of applications for this type of devices. The opportunities offered by this technique seem to be pushing new boundaries when it comes to both the use of 3D printing in practice and new materials from which the 3D objects can be printed. However, the question arises whether, at the same time, this solution is safe enough to be used without limitations, wherever and by everyone. According to the scientific reports, three-dimensional printing can pose a threat to the user, not only in terms of physical or mechanical hazards, but also through the potential emissions of chemical substances and fine particles. Thus, the presented publication collects information on the additive manufacturing, different techniques, and ways of printing with application of diverse raw materials. It presents an overview of the last 5 years' publications focusing on 3D printing, especially regarding the potential chemical and particle emission resulting from the use of such printers in both the working environment and private spaces.

Three-Dimensional (3D) Printing: Implications for Risk Assessment and Management in Occupational Settings

The widespread application of additive manufacturing (AM) technologies, commonly known as three-dimensional (3D) printing, in industrial and home-business sectors, and the expected increase in the number of workers and consumers that use these devices, have raised concerns regarding the possible health implications of 3D printing emissions. To inform the risk assessment and management processes, this review evaluates available data concerning exposure assessment in AM workplaces and possible effects of 3D printing emissions on humans identified through in vivo and in vitro models in order to inform risk assessment and management processes. Peer-reviewed literature was identified in Pubmed, Scopus, and ISI Web of Science databases. The literature demonstrated that a significant fraction of the particles released during 3D printing could be in the ultrafine size range. Depending upon the additive material composition, increased levels of metals and volatile organic compounds could be detected during AM operations, compared with background levels. AM phases, specific job tasks performed, and preventive measures adopted may all affect exposure levels. Regarding possible health effects, printer emissions were preliminary reported to affect the respiratory system of involved workers. The limited number of workplace studies, together with the great variety of AM techniques and additive materials employed, limit generalizability of exposure features. Therefore, greater scientific efforts should be focused at understanding sources, magnitudes, and possible health effects of exposures to develop suitable processes for occupational risk assessment and management of AM technologies.

Establishing Quality and Safety in Hospital-based 3D Printing Programs: Patient-first Approach

The adoption of three-dimensional (3D) printing is rapidly spreading across hospitals, and the complexity of 3D-printed models and devices is growing. While exciting, the rapid growth and increasing complexity also put patients at increased risk for potential errors and decreased quality of the final product. More than ever, a strong quality management system (QMS) must be in place to identify potential errors, mitigate those errors, and continually enhance the quality of the product that is delivered to patients. The continuous repetition of the traditional processes of care, without insight into the positive or negative impact, is ultimately detrimental to the delivery of patient care. Repetitive tasks within a process can be measured, refined, and improved and translate into high levels of quality, and the same is true within the 3D printing process. The authors share their own experiences and growing pains in building a QMS into their 3D printing processes. They highlight errors encountered along the way, how they were addressed, and how they have strived to improve consistency, facilitate communication, and replicate successes. They also describe the vital intersection of health care providers, regulatory groups, and traditional manufacturers, who contribute essential elements to a common goal of providing quality and safety to patients. ©RSNA, 2021.

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

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.

Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence

This work describes a novel methodology for the quality assessment of a Fused Filament Fabrication (FFF) 3D printing object during the printing process through AI-based Computer Vision. Specifically, Neural Networks are developed for identifying 3D printing defects during the printing process by analyzing video captured from the process. Defects are likely to occur in 3D printed objects during the printing process, with one of them being stringing; they are mostly correlated to one of the printing parameters or the object’s geometries. The defect stringing can be on a large scale and is usually located in visible parts of the object by a capturing camera. In this case, an AI model (Deep Convolutional Neural Network) was trained on images where the stringing issue is clearly displayed and deployed in a live environment to make detections and predictions on a video camera feed. In this work, we present a methodology for developing and deploying deep neural networks for the recognition of stringing. The trained model can be successfully deployed (with appropriate assembly of required hardware such as microprocessors and a camera) on a live environment. Stringing can be then recognized in line with fast speed and classification accuracy. Furthermore, this approach can be further developed in order to make adjustments to the printing process. Via this, the proposed approach can either terminate the printing process or correct parameters which are related to the identified defect.