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. 2026 Jul 15;39(8):1546–1558. doi: 10.1021/acs.chemrestox.6c00128

3D Printing Filament Composition, Emissions, and Induced Proinflammatory Responses

Jonathan M Beard †, Charli S Worth ‡, Dinny Stevens †, Amanda K Charlton-Sevcik †, John Hadynski §, Joseph H Taube ‡, Souhail R Al-Abed ∥, Christie M Sayes †,‡,*
PMCID: PMC13488613  PMID: 42457647

Abstract

This study addresses current gaps in the 3D printing literature for filament composition (polymer type, color, and brand), aerosol emissions, and human health risks. Six metals (aluminum, magnesium, manganese, chromium, iron, and copper) were found in nonmetal filaments depending on polymer type, color, and brand using inductively coupled plasma-mass spectrometry. For copper- and steel-filled filaments, scanning electron microscopy and elemental analysis indicated a higher metal concentration within the filaments than on the surface. More VOCs were emitted from acrylonitrile butadiene styrene (ABS) filaments compared to polylactic acid (PLA) filaments, according to thermal desorption unit samples analyzed by gas chromatography. Styrene, emitted from ABS filaments, appears to pose the greatest potential health risk among known VOCs, given its concentration and reported effects. Toluene and benzaldehyde were emitted in lower concentrations from both ABS and PLA filaments and may pose a potential risk to human health. Based on three inflammatory markers across two exposure times using BEAS-2B epithelial lung cells, steel showed the highest proinflammatory response, possibly due to chromium and overall particulate matter. 3D printer users should minimize aerosolized emissions by taking proper precautions, such as ensuring adequate ventilation and wearing face masks, particularly during extended exposure.

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1. Introduction

3D printing (3DP) is at the forefront of modern industrialization, driving production across multiple Fortune Global 500 companies. 3DP is becoming more commonly employed in diverse industries, such as aerospace, the automotive industry, surgery, dentistry, and food production. − It can use various materials, including clay, wood, glass, and even lunar regolith (also known as “moondust”). − Among 3DP methods, fused filament fabrication (FFF/FDM), or fused deposition modeling, has consistently been the most prevalent worldwide. FDM is mainly associated with the use of thermoplastics such as nylon, polycarbonate, polyethylene terephthalate, and, most commonly, acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA). For any 3DP scenario, the aerosol exposure profile is dependent upon the kind of filament in use, including both primary monomers and secondary thermal degradation byproducts that are unique to specific filaments. As 3DP continues to gain traction across offices, schools, and private residences, , there is a critical need to establish a thorough understanding of the effects of 3DP on indoor air quality across varying filament materials.

3DP produces aerosolized emissions consisting of particulate matter (PM) and volatile organic compounds (VOCs). Most of the emitted PM is in the ultrafine range (PM0.1), which can cause harm by depositing onto the deepest pulmonary tissues and crossing the alveolar-capillary barrier into the bloodstream. − The effects of VOC exposure depend on the individual chemical structures, which vary widely and include aromatic compounds (e.g., styrene), cyclic compounds (e.g., lactide), halogenated compounds (e.g., 1-iodododecane), and acids (e.g., acetic acid), among others. Therefore, the risk imposed by VOC exposure depends on the individual compound. While exposure to styrene could cause respiratory irritation and oxidative stress, lactide has few known adverse health effects.

Thermoplastics commonly used in FDM, especially PLA, can be reinforced with metal fillers, resulting in more complex structures composed of mixed materials. In addition to intentional metal fillers, other filaments contain metals from additives as well as contamination from factory production and extrusion through the 3D printer itself. , Inhalation exposure to metals detected in 3DP emissions, such as iron, copper, and aluminum, is associated with harm to the respiratory, cardiovascular, and nervous systems.

Both VOCs and PM, particularly metal-containing PM, have been associated with inflammation. − In the first century A.D., Celsus described the signs of inflammation as rubor, calor, dolor, and tumor (redness, heat, pain, and swelling). However, the inflammatory process begins before these visible symptoms, with both epithelial cells and macrophages releasing signaling molecules to initiate the response. When irritants, allergens, or pollutants encounter the lungs, a wide variety of receptors, categorized as pattern recognition receptors (PRRs), on the cell surfaces of airway epithelia recognize and respond to these particles. The PRR response triggers a signaling cascade resulting in the release of cytokines and chemokines responsible for inducing an immune response, in which (1) more signaling molecules are released through a positive feedback loop, (2) leukocytes are recruited to the site of injury, and (3) physiological changes necessary for repair are initiated, such as increased blood flow and endothelial cell adhesion, which, in turn, produce the classic signs of inflammation.

These signaling molecules include the well-studied collection of members of the interleukin (IL) family, as well as tumor necrosis factor alpha (TNFα) and interferons (IFNs), which further activate proteins involved in crucial downstream processes such as intercellular adhesion molecule 1 (ICAM-1) and the proinflammatory molecule leukotriene A4 hydrolase (LTA4H). While inflammation in the body, including the lungs, is a natural process used to respond to infection while initiating the healing process, pulmonary inflammation is a key component leading to airway remodeling and damage associated with diseases such as asthma, chronic obstructive pulmonary disease (COPD), silicosis, and rheumatoid arthritis. ,

This study addresses the minimal database on 3D printer emissions, especially the lack of information on filament color, the presence of metals in filaments, and their relationships with emissions and potential adverse health effects. This knowledge gap was addressed by measuring the metal and elemental profiles of each filament, followed by a downstream assessment of inflammatory transcription markers and VOC emissions to examine correlations between the filament composition and potential human health effects. Our previous work investigated the effect of primarily the PM component of 3DP emissions on cell viability and oxidative stress. This study expands on previous work by extending exposure duration, offering numerous treatments, and adding a combination of analytical techniques, thereby providing a novel contribution to the current understanding of 3DP emissions and their potential impact on indoor air quality.

2. Methods and Materials

2.1. Cell Culture

Immortalized human bronchial epithelial BEAS-2B cells (ATCC), passages 8–29, were maintained in both DMEM and RPMI media (Gibco) supplemented with 10% heat-inactivated fetal bovine serum (Corning) and 1% penicillin–streptomycin solution (100x) (Corning) in a 5% CO2 humidified atmosphere at 37 °C. Cells were plated on six-well 0.4 μm transparent PET cell culture inserts (Falcon) at least 48 h prior to exposure. Cells were plated to reach 1.2 million cells at the time of exposure, with densities of 300,000 cells 2 days prior, 150,000 cells 3 days prior, and 75,000 cells 4 days prior to exposure. Cells were taken to the air–liquid interface 24 h prior to exposure, and a second aspiration was performed upon transferring the inserts to carrier plates.

2.2. Aerosol Exposure and Filament Collection

Cells were exposed to aerosol emissions from a desktop FFF 3D printer within the CelTox Sampler, as previously described, for 1 or 4 h. Ten filaments were included in this study, including four ABS filaments (two transparent, two black), four PLA filaments (two transparent, one black, one blue), and two PLA filaments with metal filler (copper and steel). The eight nonmetal filaments were divided between two brands, “Brand 1” and “Brand 2.” Pre- and postextrusion filaments were also collected for chemical analysis. Postextrusion samples were collected into glass scintillation vials before contacting the build plate, without using microcutting pliers to avoid metal contamination. Purges were performed after loading each filament until no trace of the previous filament was visible.

2.3. Scanning Electron Microscopy Energy Dispersive X-Ray Spectroscopy (SEM-EDAX)

Focused ion beam scanning electron microscopy (FEI-Versa 3D-Thermo Fisher) was performed to characterize morphological differences pre- and postextrusion. Representative samples of filaments were placed onto double-sided carbon tape mounted on aluminum SEM specimen stubs and sputter-coated with 10 nm of iridium. Imaging was performed at an accelerating voltage of 15–30 kV. Quantitative characterization was also performed using energy-dispersive X-ray spectroscopy (EDAX APEX) to determine the elemental composition of selected areas. At least three areas were analyzed, atomic percentages were averaged, and the results were plotted using GraphPad Prism 10.

2.4. Inductively Coupled Plasma Mass Spectrometry (ICP-MS)

2.4.1. Sample Preparation and Data Collection

3D printer filaments were collected pre- and postextrusion for inductively coupled plasma-mass spectrometry (ICP-MS) analysis (Agilent 7900) in quadruplicate. Digestion methods were adapted from EPA Method 3050B and those described by Peloquin et al. Each sample contained 0.1 g of filament and was digested in 50 mL polypropylene digestion tubes (Cole-Parmer Environmental Express) at 95 °C on a polytetrafluoroethylene-coated hot block (Environmental Express HotBlock SC154 Digestion System). A total of 30 mL of concentrated nitric acid (HNO3; TraceMetal grade, Fisher Scientific), 6 mL of 30% hydrogen peroxide (H2O2; Certified ACS, Fisher Scientific), and 5 mL of concentrated hydrochloric acid (HCl; TraceMetal grade, Fisher Scientific) were added in separate portions over 15 h. A control containing just HNO3, H2O2, and HCl also underwent digestion and analysis. After cooling to room temperature, the samples were filtered, diluted to 50 mL with ultrapure water, and analyzed in helium collision mode. Steel and copper filaments were additionally diluted by a factor of 20,000x. Calibration curves were made using a multielement standard solution (PerkinElmer (N9301720)). Once a linear regression was performed on the calibration curve values, the limit of detection (LOD) was calculated as the slope divided by 3.3 times the regression’s standard error (LOD = 3.3σ/S). All values quantitatively reported were above the limit of quantification (LOQ; LOQ = 10σ/S). LOD and LOQ values for each metal are summarized in Table S1.

2.4.2. ICP-MS Statistical Analysis

The statistical significance of the difference between pre- and postextrusion was determined using an unpaired t-test. Within each metal and extrusion group, statistical significance was assessed using a one-way analysis of variance (ANOVA; α = 0.05), followed by Dunnett’s T3 multiple comparisons test. The Brown-Forsythe test was used to confirm the equality of variances. Shapiro–Wilk tests were conducted to assess whether the data sets were normally distributed. All statistical analyses were completed on normally distributed data using GraphPad Prism 10. Asterisks indicate the following p-value ranges: * 0.01–0.05, ** 0.001–0.01, *** 0.0001–0.001, **** < 0.0001.

2.5. Volatile Organic Compounds (VOCs) Collection and Gas Chromatography–Mass Spectrometry (GC–MS)

Emission samples were pumped onto Tenax TA sorption tubes (CAMSCO) at a flow rate of 500 mL/min for 60 min. Sorption tubes were spiked with 10 μL of a standard solution containing 150 μg/mL each of the internal standards: d5-styrene, d8-naphthalene, and d22-decane (Absolute Standards Inc.) in methanol. A calibration curve was prepared using methanolic solutions of a VOC mix at increasing concentrations, with each internal deuterated standard listed above maintained at a constant concentration. A Thermal Desorption Unit (TDU) 2 system and multipurpose sampler (Gerstel) were used to desorb volatiles from the Tenax TA sorption tubes at 280 °C for 3 min with a ramp rate of 720 °C/min, following a solvent vent step at 50 °C for 3 min with a ramp rate of 60 °C/min, and concentrated using a CIS cryotrap system (Gerstel). The samples were split 100:1 and injected into an Agilent 8890 GC equipped with a DB-624 column (Agilent). Analysis was performed with an Agilent 7010 GC/TQ MS. Target compound identification and quantitative analysis were performed with Agilent MassHunter Quantitative Analysis 10.0 and NIST MS Search 2.4. A calibration curve was prepared using a mixture of 74 VOCs, while the remaining 56 identified VOCs were measured semiquantitatively in Agilent MassHunter based on the closest retention time (VOCs measured either quantitatively (Q) or semiquantitatively (S) are labeled in Table S3). Background samples were collected in the chamber without the 3D printer running, and the concentration of each VOC (ng) was subtracted from the value measured for each filament. The final quantity was measured in ng VOC per kg filament.

2.6. RT-qPCR (Reverse-Transcriptase Quantitative Polymerase Chain Reaction)

2.6.1. RNA Collection, cDNA Synthesis, and Data Collection

Total RNA was immediately isolated from cultured cells using TRIzol extraction (Thermo Scientific) according to the manufacturer’s guidelines, following one- or four-hour aerosol exposures. Complementary DNA was synthesized from 1 μg of RNA using a High-Capacity cDNA Reverse Transcription Kit (Thermo Scientific) as per the manufacturer’s recommendations. Primers for ICAM1, IL1R1, LTA4H, and GAPDH were obtained from Integrated DNA Technologies, with GAPDH used as the internal control for normalization. Markers were chosen based on results from a preliminary qPCR panel (Table S2).

ICAM1 F: 5′-GGAGCCAATTTCTCGTGCCG-3′

ICAM1 R: 5′-GCGTGTCCACCTCTAGGACC-3′

IL1R1 F: 5′-AGGGAGCGGCAGGAATGTG-3′

IL1R1 R: 5′-ACCATCTTCAGAGGGTGCGTC-3′

LTA4H F: 5′-ACTGCAGAGGTGTCTGTCCC-3′

LTA4H R: 5′-CTTGGGCCAATTTGCCTGCT-3′

GAPDH F: 5′-GGAGCGAGATCCCTCCAAAAT-3′

GAPDH R: 5′-GGCTGTTGTCATACTTCTCATGG-3′

AzuraView GreenFast qPCR Blue Mix LR (Azura Genomics) and a QuantStudio 5 (Thermo Scientific) were utilized for the quantitative polymerase chain reaction (qPCR). Relative quantification values were calculated using the ddCt method and plotted with SEM by using GraphPad Prism 10. All samples were run in biological triplicates and in technical quadruplicates.

2.6.2. RT-qPCR Statistical Analysis

Statistical analysis was performed using GraphPad Prism 10, employing a two-way ANOVA (α = 0.05), followed by Dunnett’s multiple comparisons test. Fold change for each treatment group was normalized to the respective untreated vehicle control (exposed to air alone) for each exposure time (1 or 4 h). Asterisks indicate the following p-value ranges: * 0.01–0.05, ** 0.001–0.01, *** 0.0001–0.001, **** < 0.0001.

3. Results and Discussion

3.1. Micrographs and Elemental Analysis

Filaments collected before and after extrusion through the 3D printer were mounted and sputter-coated for SEM imaging and subsequent EDAX. Across all filaments, postextrusion morphological changes included reduced filament diameter and altered surface texture (Figures , S1, and S2). This difference in surface texture, in which postextrusion filaments appear less rigid due to melting and cooling, was exacerbated in the metal-containing filaments (Figure ). Copper and steel filaments both exhibited significantly greater consistency in the distribution of metal particles within the filaments prior to extrusion, as evidenced by cross sections and lateral views. However, the surface accessibility of these metal particles decreased after extrusion as the filament surface became smoother and more uniform, with fewer particles protruding beyond the plastic matrix.

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SEM micrographs for copper and steel filaments pre- and postextrusion. Pre-extrusion images provide cross-sectional and lateral perspectives for comparing the filament surface and internal composition.

EDAX revealed that all filaments were primarily composed of carbon and oxygen, with PLA filaments exhibiting a higher oxygen content (Figure ). Copper was also detected only in the copper-filled filament, and chromium and iron were only detected in the steel-containing filament. Phosphorus was present (above 0.2%) in one filament (“Copper” pre-extrusion). Silicon and calcium were present in some filaments, but never above the 0.2% threshold (chosen based on graph visibility). Iridium was found in all filaments due to sputter coating. The extrusion process did not change the overall elemental composition.

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EDAX for all filaments pre- and postextrusion, showing percentage of elements detected, including carbon, oxygen, copper, iron, chromium, and phosphorus. Elements were included if greater than 0.2% (silicon and calcium were detected below the threshold). Iridium was excluded as a sputter-coating product.

The higher carbon-to-oxygen ratio in ABS filaments and the higher oxygen ratio in PLA are corroborated by previous studies. − The different carbon:oxygen ratios between ABS and PLA can also be explained by the chemical composition of the polymers, in which PLA contains oxygen in its lactic monomer. Nitrogen was not observed in the ABS filaments despite the known presence of a nitrile group specifically in the acrylonitrile monomer. The absence or trace presence of nitrogen has been seen in other studies; − however, one study has shown nitrogen to be as high as 17% and as low as 0.21% across four ABS filaments. The varying degrees of nitrogen present are perhaps best explained by the inconsistency of ABS as a polymer.

While a high concentration of metal particles has been observed and measured in the cross section of metal-filled filaments, ,− lower concentrations of metals may exist on the surfaces of these same filaments. In both metal filaments of the present study, metal particles were qualitatively observed in the cross section, as seen in both the pre- and postextrusion lateral surface perspectives. The EDAX results, taken from the lateral perspective, also measured at most 10% metal composition in either the copper or steel filaments. These lower surface metal concentrations could affect the surface chemistry of the filament, possibly decreasing the risk of dermal exposure to 3D-printed objects with metal fillers, when comparing the available metal measurements with those made at the cross section or in digested filaments. Whether this would also affect the metal concentration in aerosolized particles is unclear. Still, it could explain why aerosolized metal emissions from filaments have been quantified at most only at very low levels. − In the current study, SEM-EDAX analysis of particle emissions was unsuccessful, and the authors agree with prior work that, due to the low recovery of emitted particles during 3DP, this method is likely not well-suited for analyzing both filaments and particles. , Future studies are encouraged to pursue high-resolution methods specifically suited toward quantifying the metal content in emitted particles during the printing process, such as combined filter collection and ICP-MS analysis.

3.2. Trace Metal Analysis

Filaments collected before and after extrusion were digested for ICP-MS analysis, and all metal concentrations measured above the limit of quantitation (LOQ) are shown (Table ). Of the 12 metals tested, six were detected above the LOQ for some filaments (magnesium, aluminum, manganese, copper, iron, and chromium), and six were not (vanadium, zinc, nickel, cobalt, cadmium, and lead). The difference in elemental composition was compared between each filament (Figure ), and the change in elemental composition within each filament due to extrusion was also measured (Figure ). Magnesium was found in all filaments except for “1 PLA Black,” “Copper,” and “Steel.” It was measured at the highest concentration in “1 ABS Black,” especially before extrusion (Figure ), which was significantly higher than the concentration after extrusion (Figure A). In three other filaments, the postextrusion concentration of magnesium was significantly higher than that before extrusion (Figure ). Aluminum was only detected in two filaments (“2 PLA Blue” and “2 PLA Trans”) and was significantly higher in “2 PLA Blue” both before and after extrusion (Figure ). Within “2 PLA Blue,” the concentration of aluminum was significantly higher pre-extrusion (Figure B). Manganese was only found in the pre-extrusion measurement of four filaments at a low concentration slightly above the LOQ. Copper and iron were found in most filaments and, as expected, were significantly higher in the copper and steel filaments, respectively, in the postextrusion measurements (Figure ). When comparing pre- and postextrusion quantities, copper and iron concentrations were significantly higher postextrusion across multiple filaments (Figure D and E). Chromium was found in three filaments postextrusion and had the highest concentration in “Steel” (Figure ), which had chromium in both pre- and postextrusion samples.

1. Metal Concentration as Mean ± SD for All Filaments Pre-Extrusion and Postextrusion, with Measurements below the Limit of Quantitation Marked with “―”.

    Elemental Analysis (ng element/mg filament)
    24Mg 27Al 52Cr 55Mn 56Fe 63Cu
1 ABS Transparent Pre 23.1 ± 7.80 ― ― ― 5.95 ± 5.11 6.54 ± 4.41
Post ― ― 35.1 ± 14.4 ― 36.2 ± 12.1 86.7 ± 10.8
1 ABS Black Pre 229 ± 8.76 ― ― 0.63 ± 0.57 7.33 ± 7.44 ―
Post 180 ± 6.46 ― ― ― 35.9 ± 15.3 9.76 ± 4.67
2 ABS Transparent Pre 27.8 ± 5.36 ― ― 0.75 ± 0.16 4.36 ± 1.78 ―
Post 16.7 ± 7.23 ― ― ― 20.7 ± 5.89 13.3 ± 8.90
2 ABS Black Pre 20.3 ± 6.59 ― ― 0.34 ± 0.02 1.97 ± 1.33 12.5 ± 4.55
Post 23.1 ± 9.12 ― ― ― 66.8 ± 1.72 7.85 ± 2.25
1 PLA Transparent Pre 16.8 ± 3.63 1.53 ± 1.59 ― 0.53 ± 0.24 6.09 ± 5.58 ―
Post 108 ± 39.1 ― ― ― ― 38.6 ± 17.8
1 PLA Black Pre ― ― ― ― ― 17.0 ± 10.9
Post ― ― ― ― ― 40.7 ± 2.49
2 PLA Transparent Pre 33.6 ± 1.78 2.33 ± 0.22 ― ― 1.85 ± 0.55 ―
Post 143 ± 26.6 9.94 ± 2.02 ― ― ― 34.5 ± 3.76
2 PLA Blue Pre 14.9 ± 10.8 293 ± 7.55 ― ― 3.78 ± 0.97 4.40 ± 3.48
Post 94.1 ± 46.0 240 ± 15.1 29.7 ± 7.62 ― 218 ± 53.2 30.0 ± 9.95
Copper Pre ― ― ― ― ― 9.18e5 ± 3.52e5
Post ― ― ― ― ― 8.60e5 ± 5.72e4
Steel Pre ― ― 1.42e5 ± 8.06e4 ― 5.39e5 ± 1.30e5 ―
Post ― ― 1.21e5 ± 5.85e4 ― 8.12e5 ± 8.48e4 ―
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Inductively coupled plasma-mass spectrometry trace metal analysis for all filaments (A) pre-extrusion and (B) postextrusion, comparing magnesium, aluminum, iron, and copper concentrations. Data graphed as mean ± SD. Concentrations of each element across each filament within either pre- or postextrusion groups were statistically compared on GraphPad Prism using a one-way ANOVA followed by Dunnett’s T3 multiple comparisons test. Bars with different letters indicate statistically significant differences.

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Inductively coupled plasma-mass spectrometry comparing (A) magnesium, (B) aluminum, (C) manganese, (D) copper, (E) iron, and (F) chromium concentrations pre- (blue) and postextrusion (purple) across all ten filaments. Data graphed as mean ± SD. Pre vs post within each filament group for each element was statistically compared using an unpaired t-test in GraphPad Prism (p < 0.05: *, p < 0.01: **, p < 0.001: ***, p < 0.0001: ****).

All six metals detected in the present study have been previously recorded in nonmetal-filler 3DP filaments. ,,− According to Peloquin et al. (2022), both aluminum and magnesium were detected at higher levels in ABS filaments. The results also showed higher magnesium levels in ABS, especially in brand 1 ABS black, but there were no higher aluminum levels in ABS filaments. However, Peloquin et al. also concluded that color was a determining factor in certain metal concentrations, with blue filaments having the highest aluminum levels. Since our only blue filament was PLA, this likely affected the comparison between ABS and PLA for aluminum. Color may be a stronger driver of metal concentration than the type of plastic due to the specific additives used to produce each color. In the 13 cases where there was a significant difference between pre- and postextrusion samples, the postextrusion metal contents were significantly higher in 11 of those instances (Al, Mg, Cu, Fe); thus, supporting the conclusion that more metals are adsorbed to filaments as they are excreted through the nozzle of 3D printers. The filament brand also seems to affect metal concentrations as well as plastic type and color, especially for magnesium and copper (and possibly manganese and chromium). Total 3DP emissions have previously been shown to correlate with filament brand. ,,,

3.3. VOC Emissions and Potential Health Effects

Emission samples were collected on sorption tubes, desorbed with a TDU-2, and analyzed by gas chromatography–mass spectrometry (GC-MS) to compare the possible VOCs aerosolized between filaments. The total VOC emissions were higher for the four ABS filaments than for the PLA filaments. The summed ng VOC/kg filament for the 12 highest-emitted VOCs were 426,760, 752,602, 827,935, and 671,409 for “1 ABS Trans,” “1 ABS Black,” “2 ABS Trans,” and “2 ABS Black,” respectively. Of the remaining filaments, “1 PLA Black” had the largest total emissions, with a sum of 273,551 ng VOC/kg filament. The remaining filaments ranged from 11,692 (“1 PLA Trans”) to 71,229 (“2 PLA Blue”). The 12 highest-emitted VOCs from each filament (ng/kg) are shown in Figure and are abbreviated based on chemical grouping (“Ar” = aromatic, “Al” = aliphatic, “C” = cyclic, “H” = halogenated, “N” = nitrogenous; full names for all chemicals are found in Table S3). Full emission quantities are provided in Table S4.

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Proportion of the 12 most emitted VOCs (ng VOC/kg filament) by aerosolization of each filament through thermal desorption, followed by GC-MS. Full chemical names and CAS numbers for all compounds are found in Table S3. Al = aliphatic, Ar = aromatic, C = cyclic, H = halogenated, N = nitrogenous. Emission quantities are found in Table S4.

Throughout the VOC composition analysis across all ten filaments, aromatic compounds predominated, with a total of 20 unique aromatic compounds detected. The aliphatic group produced the second-highest number of compounds, 11, followed by seven cyclic compounds, two halogenated compounds, and a single nitrogenous VOC. Styrene (Ar1) was predominantly found in the two ABS filaments belonging to the second brand, whereas ethylbenzene (Ar2) was commonly present in all four ABS filaments. Phenylsuccinic acid (Ar3) was emitted by three of the ABS filaments and was the most emitted compound within the ABS black filament of Brand 1. Acetophenone (Ar4), toluene (Ar5), benzaldehyde (Ar6), and methyl methacrylate (Al2) were found in high proportions across the ABS and PLA filaments. l-Lactide (C1) was the most detected VOC overall, especially across the two PLA filaments with a color additive and the copper filament. The single nitrogenous VOC, 2-maleimidoacetic acid, was strongly detected in three of the PLA filaments, with only a lower reading in the steel filament. Pivalic acid (Al3), 1,6-dioxacyclododecane-7,12-dione (C2), and cyclopentanone (C3) were also detected in the copper and steel filaments.

The prevalence of styrene emissions from ABS filaments is well documented in the literature. ,, Styrene was the predominant VOC released from brand 2 ABS filaments, but not from brand 1, again showing that overall 3DP emissions vary by brand rather than by polymer type or color. This is also likely due to the inherent inconsistency of ABS as a polymer. Styrene has an acute inhalation lethal toxic concentration (LC50) value of 2770 ppm (2 h, rat). It can irritate the skin and eyes, is a probable human carcinogen according to the IARC classification (Group 2A as of 2019), and has also been shown to increase the risk of pulmonary infection in infants in one case study. − Ethylbenzene has also been detected in ABS filament emissions, has an LC50 of 4000 ppm (4 h, rat), is possibly carcinogenic to humans with a no significant risk level (NSRL) of 54 μg/day (inhalation), and has significantly affected fetal development in rats at 1000 and 2000 ppm. − ,,− Acetophenone, detected in both ABS and PLA filaments in the present study, has previously been associated with ABS filament emissions. , The toxicity of acetophenone is not well-defined in the literature, with an LC50 value of >430 ppm and lacking information on potential pulmonary effects; but it could, like styrene, cause dermal and ocular irritation.

Toluene was produced in similar proportions across ABS and PLA filaments, consistent with previous findings. ,, The known LC50 value of toluene is relatively high at 8800 ppm (4 h, rat). Low-concentration exposures can cause upper respiratory and ocular irritation in humans, and high-concentration, chronic exposures can cause cardiac arrhythmias, impair fetal development, and induce epithelial inflammation. − Benzaldehyde was produced by most filaments tested. While its LC50 value is not well-defined in rats (>1000 ppm, 6 h), it has been shown to produce severe health effects in rats in subacute inhalation studies and is among the “most hazardous VOCs” from 3DP. − Both lactide and methyl methacrylate are known PLA emissions that were unexpectedly found in both PLA and ABS filament emissions in the present study, and methacrylates are associated with some respiratory effects. ,,

In the copper filament and especially the steel filament, cyclic compounds 1,6-dioxacyclododecane-7,12-dione and cyclopentanone were emitted in high amounts, but the LC50 values are unknown and >5700 ppm (4 h, rat), respectively. Little is known about the health effects of 1,6-dioxacyclododecane-7,12-dione health effects other than its GHS classification as a respiratory irritant; cyclopentanone is not a known sensitizer but is a mild dermal irritant. Pivalic acid was produced in roughly equal amounts in the metal filaments. While little is known about its health effects, it negatively affected skeletal muscle in dogs but not in rats due to species-specific metabolism of the compound.

Both NIOSH and OSHA provide reference databases of potential inhalation exposure limits for a variety of compounds in occupational settings, expressed as recommended exposure limits (RELs, NIOSH) or permissible exposure limits (PELs, OSHA). However, only four VOCs discussed above are present in either database: styrene, ethylbenzene, toluene, and methyl methacrylate. Furthermore, the VOC concentrations in the present study are relative to the mass of printed filament rather than to the volume of air. Thus, comparable VOC values (ppm) were estimated using the mass of printed material (kg/h) and the flow rate (L/h). The highest estimated value for each VOC from the present study was 1.53 × 10–4 ppm styrene (2 ABS Trans), 1.14 × 10–4 ppm ethylbenzene (1 ABS Trans), 7.60 × 10–5 ppm toluene (1 ABS Black), and 9.00 × 10–5 ppm methyl methacrylate (1 PLA Black). The stated exposure limits are 50–100 ppm (NIOSH) and 100–600 ppm (OSHA) for styrene, 100–125 ppm (NIOSH) and 100 ppm (OSHA) for ethylbenzene, 100–150 ppm (NIOSH) and 200–500 ppm (OSHA) for toluene, and 100 ppm (NIOSH & OSHA) for methyl methacrylate. , Thus, none of these four VOCs pose an acute inhalation risk compared with either database, as determined by their estimated ppm values for 1 hour of printing at a flow rate of 30 L/h.

The EPA Integrated Risk Information System (IRIS) provides reference concentrations (RfCs) for continuous, chronic inhalation exposure expected to produce no or low observed adverse effects. IRIS lists an RfC of 0.23 ppm for both styrene and ethylbenzene, 1.33 ppm for toluene, and 0.17 ppm for methyl methacrylate. While comparison with this data set has the same two obstacles as comparison with the NIOSH and OSHA databases, it does highlight greater sensitivity in exposure risk for the same compounds in a chronic versus an acute setting. None of the listed reference values were lower than the estimated VOC concentrations in ppm given above, indicating little to no expected risk for these four VOCs. However, these values are not direct measurements, were estimated for only a one-hour exposure, and do not cover most of the VOCs detected in this study. Thus, caution should still be exercised to minimize chronic exposure to aerosolized emissions from 3D-printing.

3.4. Proinflammatory Response

RNA was extracted from exposed bronchial epithelial cells, converted to cDNA, and the inflammatory response induced by 3D-printer-generated aerosols was evaluated by qPCR. The ten filaments were compared to the control group after 1 hour (Figure A) and 4 hours (Figure B) of exposure, measuring the expression of three inflammatory markers: ICAM1, IL1R1, and LTA4H (normalized to GAPDH). Among the one-hour exposure groups, none of the treatments showed significant differences in ICAM1. However, exposure to emissions from “1 PLA Transparent” and “2 PLA Transparent” induced significant upregulation of IL1R1, and exposure to emissions from “1 ABS Black,” “1 PLA Black,” and “Steel” induced significant upregulation of LTA4H in the same cell line. In the four-hour exposure, the only significant difference was found in the ICAM1 marker, with upregulation after exposure to “Steel.”

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RT-qPCR of inflammatory markers ICAM1, IL1R1, and LTA4H normalized to GAPDH as mean ± SEM (n = 3). BEAS-2B cells exposed to emissions from respective filaments in a CelTox sampler for A) one hour or B) four hours. Fold changes are shown relative to an untreated vehicle control exposed to the CelTox sampler for an equivalent duration. Statistical analysis was performed on GraphPad Prism using two-way ANOVA significance testing with multiple comparisons (* 0.01–0.05, ** 0.001–0.01, *** 0.0001–0.001, **** < 0.0001).

Across the five filaments that produced significant changes in proinflammatory gene expression, there is no evident correlation in their VOC emission profiles. Furthermore, there is currently no reported evidence that the highest-emitting VOCs (e.g., styrene, ethylbenzene, lactide, 2-maleimidoacetic acid, 1,6-dioxacyclododecane-7,12-dione) are either known sensitizers or otherwise associated with an inflammatory response. While many of the VOCs detected here are not well studied, the currently available information does not provide convincing evidence that any individual VOC is responsible for an acute inflammatory response. It is therefore more likely that the overall filament emissions, including PM, could induce upregulation in inflammatory markers. Four filaments produced a significant increase in one inflammatory marker in the one-hour exposure, but not in the four-hour exposure. It is possible that the initial exposure to the 3DP-emitted aerosol produced an immediate immune response that began to resolve by the four-hour time point, although differential expression of inflammatory response genes has been observed between the one- and four-hour time points. “Steel” was the only filament that produced an increased response at both time points and with more than one marker. While iron has generally elicited lower inflammatory responses than copper, chromium exposure, even at low doses, has been linked to oxidative stress and inflammation. − Therefore, the chromium in the steel filament could have contributed to the observed inflammatory response. Chromium­(VI) also has the lowest RfC in the IRIS database compared with the four VOCs discussed above, at 0.00001 ppm (neither iron nor copper has an RfC value). In our previous work, “Steel” was one of only two filaments that significantly reduced cell viability, as measured by MTS, among the 17 tested filaments, and it also significantly reduced glutathione concentration.

Limitations of this study include the use of only a single brand of PLA blue and steel filaments, which limits the extent to which conclusions can be drawn about these additives. Future studies are needed to compare multiple brands of each filament type to elucidate the metal additives expected in blue filaments and to characterize the inflammatory response associated with steel-filled filaments. A higher concentration of metal additives would be expected to leach into the liquid from filament samples than to aerosolize during printing; thus, future experimental designs could combine liquid–liquid interface exposures with air–liquid interface exposures to further explore the effects of metal additives. Finally, the lack of a robust increase in inflammatory markers may be due to the postexposure interval before RNA extraction; future studies could extend this interval to assess whether a more latent inflammatory response occurs. Future work could investigate the immune response to 3DP emissions by including additional inflammatory markers, such as IL6, IL8, TNF, and IL1B, and by using coculture models with immune cells, such as macrophages, to better capture these biologically relevant signaling molecules. Overall, total inhalation exposure to 3D printing emissions should be minimized, with room ventilation (residential) or PPE (occupational) being minimal precautions to reduce chronic exposure to 3D printer emissions.

Supplementary Material

tx6c00128_si_001.pdf (1MB, pdf)

Acknowledgments

The authors would like to acknowledge the Center for Microscopy and Imaging (CMI) at Baylor University and the Baylor University Mass Spectrometry Center (BU-MSC). The graphical abstract was created with an element from BioRender.

Data will be made available upon request.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.chemrestox.6c00128.

  • Low magnification SEM micrographs (Figure S1); high magnification SEM micrographs (Figure S2); LOD and LOQ values (Table S1); full qPCR array and isolated RQ values (Table S2); list of chemical names and CAS numbers (Table S3); and number of VOC emissions from Figure (Table S4) (PDF)

J.M.B. Conceptualization, Investigation, Methodology, Supervision, Visualization, Writingoriginal draft, Writingreview and editing. C.S.W.: Formal analysis, Investigation, Methodology, Visualization, Writingoriginal draft, Writingreview and editing. D.S.: Formal analysis, Investigation, Methodology, Visualization, Writingoriginal draft, Writingreview and editing. A.K.C.S.: Investigation, Methodology, Visualization, Writingoriginal draft, Writingreview and editing. J.H.: Investigation, Methodology, Writingoriginal draft, Writingreview and editing. J.H.T.: Conceptualization, Methodology, Project management, Resources, Writingreview and editing. S.R.A.A.: Conceptualization, Funding acquisition, Project management, Writingreview and editing. C.M.S.: Conceptualization, Funding acquisition, Methodology, Project management, Resources, Writingoriginal draft, Writingreview and editing. All authors have read and approved the manuscript for publication in this journal.

This research was funded and conducted by the Center for Environmental Solutions and Emergency Response (CESER) of the U.S. Environmental Protection Agency (EPA), Cincinnati, OH, in collaboration with Baylor University. This work was supported, in part, by an interagency agreement between the EPA and the U.S. Consumer Product Safety Commission (CPSC).

This work has been subjected to EPA administrative and quality assurance review and approved for publication. This work has not been reviewed or approved by the CPSC or the EPA and does not represent the views of either agency. The mention of trade names or products does not constitute endorsement or recommendation for use.

The authors declare no competing financial interest.

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