3D Microstructures Research Articles

Influence of contouring on the microstructure and micromechanical properties of Laser Powder Bed Fusion (LPBF) processed Al6061 enhanced with reactive particles

Age-hardenable aluminum alloys are of interest for powder bed fusion (PBF) additive manufacturing to produce lightweight, high-performance custom components. The successful processing of these alloys depends on specific alloying and print techniques to overcome defects that significantly degrade mechanical properties of PBF aluminum. The Reactive Additive Manufacturing (RAM) process modifieds alloy feedstock with reactive precursors which inoculate equiaxed microstructures during solidification, resulting in a reduction of columnar grain growth and shrinkage cracks. Additionally, surface quality is addressed by contouring, which retraces the perimeters of printed layers to produce smooth, fully fused surfaces. However, the combination of RAM and contouring methods has not been previously described in the literature, raising questions about the extent of refinement which is achieved when RAM particles are remelted by contouring laser sweeps. In this research, X-ray microcomputed tomography (XCT) was used to provide a complete characterization of RAM particles and defects in an AA6061-RAM alloy in 3D. The non-destructive XCT technique was used to quantify pore and particle distributions in the contoured alloy volumes and compared directly to hatched regions. The resulting three-dimensional analysis was correlated to traditional 2D microstructure quantification and nanoindentation experiments, providing a multimodal quantitative description of sub-micron grains, reaction products, and micromechanical properties. The study reveals that RAM particles inhibit cracking while simultaneously reducing the variance in mechanical properties between hatched and contoured regions of the build volume.

Multifunctional Hydrogel with 3D Printability, Fluorescence, Biodegradability, and Biocompatibility for Biomedical Microrobots.

As micron-sized objects, mobile microrobots have shown significant potential for future biomedical applications, such as targeted drug delivery and minimally invasive surgery. However, to make these microrobots viable for clinical applications, several crucial aspects should be implemented, including customizability, motion-controllability, imageability, biodegradability, and biocompatibility. Developing materials to meet these requirements is of utmost importance. Here, a gelatin methacryloyl (GelMA) and (2-(4-vinylphenyl)ethene-1,1,2-triyl)tribenzene (TPEMA)-based multifunctional hydrogel with 3D printability, fluorescence imageability, biodegradability, and biocompatibility is demonstrated. By using 3D direct laser writing method, the hydrogel exhibits its versatility in the customization and fabrication of 3D microstructures. Spherical hydrogel microrobots were fabricated and decorated with magnetic nanoparticles on their surface to render them magnetically responsive, and have demonstrated excellent movement performance and motion controllability. The hydrogel microstructures also represented excellent drug loading/release capacity and degradability by using collagenase, along with stable fluorescence properties. Moreover, cytotoxicity assays showed that the hydrogel was non-toxic, as well as able to support cell attachment and growth, indicating excellent biocompatibility of the hydrogel. The developed multifunctional hydrogel exhibits great potential for biomedical microrobots that are integrated with customizability, 3D printability, motion controllability, drug delivery capacity, fluorescence imageability, degradability, and biocompatibility, thus being able to realize the real in vivo biomedical applications of microrobots.

Rapid detection of rare events from in situX-ray diffraction data using machine learning.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Machine-Learning-Based Characterization and Inverse Design of Metamaterials.

Metamaterials, characterized by unique structures, exhibit exceptional properties applicable across various domains. Traditional methods like experiments and finite-element methods (FEM) have been extensively utilized to characterize these properties. However, exploring an extensive range of structures using these methods for designing desired structures with excellent properties can be time-intensive. This paper formulates a machine-learning-based approach to expedite predicting effective metamaterial properties, leading to the discovery of microstructures with diverse and outstanding characteristics. The process involves constructing 2D and 3D microstructures, encompassing porous materials, solid-solid-based materials, and fluid-solid-based materials. Finite-element methods are then employed to determine the effective properties of metamaterials. Subsequently, the Random Forest (RF) algorithm is applied for training and predicting effective properties. Additionally, the Aquila Optimizer (AO) method is employed for a multiple optimization task in inverse design. The regression model generates accurate estimation with a coefficient of determination higher than 0.98, a mean absolute percentage error lower than 0.088, and a root mean square error lower than 0.03, indicating that the machine-learning-based method can accurately characterize the metamaterial properties. An optimized structure with a high Young's modulus and low thermal conductivity is designed by AO within the first 30 iterations. This approach accelerates simulating the effective properties of metamaterials and can design microstructures with multiple excellent performances. The work offers guidance to design microstructures in various practical applications such as vibration energy absorbers.

Manganese cobalt sulfide nanoparticles wrapped by reduced graphene oxide: a fascinating nanocomposite as an efficient electrochemical sensing platform for vanillin determination

In this work, a fascinating nanocomposite based on manganese-cobalt sulfide (MnS/Co3S4) wrapped by electrochemically reduced graphene oxide (ERGO) has been successfully synthesized on the surface of a glassy carbon electrode (GCE) by a facile hydrothermal assisted electrochemical reduction method. The modified electrode was fully characterized by scanning electron microscopy, cyclic voltammetry and electrochemical impedance spectroscopy. The morphological results illustrate that MnS/Co3S4 are embedded in the ERGO layers, resulting in a rough surface and three-dimensional (3D) microstructure. The as-synthesized MnS/Co3S4-ERGO/GCE displays distinctly enhanced electrocatalytic activity for vanillin oxidation in comparison with that of the ERGO/GCE and MnS/Co3S4/GCE. Therefore, the MnS/Co3S4-ERGO/GCE can be used as an effective electrochemical sensing platform for the sensitive determination of vanillin, and the fabricated sensor displays a wide linear range of 0.02−1.00 μmol/L and 1.0−40.0 μmol/L, low detection limit of 4.0 nmol/L and satisfactory recoveries between 98.0% and 102.8%.

Prediction of the dynamic pressure drop of filter media loaded with water mists based on the deposited droplet mass distributions and pore network model

Predicting the pressure drop of a filter is necessary, especially regarding the jump in the dynamic pressure drop of a filter without pre-filters when clogged by rain or fog droplets. However, the efficient prediction method for the pressure drop in cylinder filters loading with water mists in the pore network faces limitations due to the high requirements for microscopic images of materials and the need to consider for the increasing holding droplet capacity of filters. In this study, a systematic method that takes into account the distributions of the deposited droplet mass within the water-absorbed filter medium layers in the pore network model (PNM) was developed. The pore geometry and distributions were determined from 2D microstructure images, and the dynamic deposited droplet mass and water-absorbing capacity of fibers were combined with the changing saturation to optimize the PNM for calculating the dynamic pressure drop of the filter media. Specifically, the empirical equation of the self-defined equivalent single fiber filtration efficiency (ESFFE) describes the relationship between the changing single fiber filtration efficiency and the deposited droplet mass. The optimized PNM is suitable for predicting the behavior of bibulous wood cellulose fiber filter media. The dynamic pressure drop shows a slight increase before surging, following the typical “channel and jump” model. Although the calculations deviate slightly from the experimental results with 25 % deviations, this systematic method effectively predict the jump in pressure drop of the tested filter medium. Therefore, this systematic method for predicting pressure drop of filters holds promise in anticipating unsafe pressure drop jumps in filters installed in coastal areas.

Mechanical property evaluation of 3D multi-phase cement paste microstructures reconstructed using generative adversarial networks

This study proposes an artificial intelligence based framework for reconstructing the 3D multi-phase cement paste microstructure to evaluate its mechanical properties using simulation. The reconstruction of cement paste microstructures is performed using modified generative adversarial networks (GANs) based on microstructural images from micro-CT. For computational efficiency, 2D microstructures are first reconstructed and then extended to 3D microstructures. The reconstructed microstructures exhibit the same microstructural features as the original microstructures when characterized by probability functions. Mechanical properties such as stiffness and tensile strength are evaluated for the original and reconstructed specimens using a phase-field fracture model, and similar behaviors are observed. The results confirm that the reconstructed virtual microstructures can be used to supplement the real microstructures in evaluating the mechanical properties of 3D multi-phase cement paste. This approach thus provides a critical element of a data-driven approach to correlating its microstructure and properties.

Hardness and microstructure of FDM 3D printed parts using self-made PLA-brass filaments

Technological advancements in the industrial sector have led to rapid developments in 3D printing technology, enabling the creation of three-dimensional prototype models. Various filaments, including polyethylene terephthalate glycol, nylon, and polylactic acid, have been widely adopted in the industry. However, filaments composed of metal mixtures are relatively scarce in Indonesia, primarily available only through select online shops worldwide. The production and sale of such filaments present lucrative opportunities within the manufacturing industry. In this research, an experimental study was conducted to examine the hardness of test specimens fabricated using PLA-brass filament. The objective was to identify the optimal hardness value of the specimens. The study focused on three key parameters: nozzle temperature, layer height, and print speed, each at two different levels. The Taguchi L4(2³) experimental design was employed, along with S/N ratio and ANOVA analysis, to evaluate the results. The findings revealed that specific combinations of parameters yield favorable hardness values, as determined by the Taguchi Method. The optimal set of parameters for achieving good hardness values was determined to be a nozzle temperature of 230°C, a layer height of 0.2 mm, and a print speed of 40 mm/s. These results enhance the understanding of PLA-brass filament properties and facilitate the utilization of 3D printing technology in the manufacturing industry.

Ultralight, dual-conductive, all-fiber based 3D anode current collector for anode-free lithium metal battery

The anode-free Li metal batteries (AFLMBs), by pairing Li-containing cathode with bare anode current collector (CC), have the potential to enable the maximum improvement (>70 %) in energy density of lithium batteries. Here, we report an ultralight, dual-conductive all-fiber based Cu–CNF-CNT as anode CC, which takes advantage of the high electronic conductivity of carbon nanotube (CNT), good ionic conductivity of copper-coordinated cellulose nanofiber (Cu–CNF), and the three-dimensional porous structure by nanofibers. The interactions between CNTs and Cu-CNFs enables good mechanical strength (4.71 MPa), high electrical conductivity (31.7 S/cm), low density (1.1 g/cm3) and good wettability with electrolyte. The 3D microstructure effectively homogenizes the Li+ flux and buffers the volume changes during cycling, leading to stable Li plating/stripping. As a result, AFLMB using Cu–CNF-CNT exhibits improved capacity retention by 33 % compared to that using Cu CC. The significantly elevated energy-density shows great application potential of the Cu–CNF-CNT in AFLMBs.

Hardening properties and microstructure of 3D printed engineered cementitious composites based on limestone calcined clay cement

Comparison of three-dimensional (3D) printing and cast methods on the mechanical properties of cementitious materials is the basis for intelligent construction development. To achieve this, a series of experiments including the uniaxial tensile, compressive, flexural and interlayer bonding tests of mould-cast and 3D printed (3DP) specimens are conducted to investigate the effects of different preparation technologies on the microstructure and hardening properties of limestone calcined clay cement (LC3) based engineered cementitious composites (ECC). Results indicate that the tensile strength and strain capacity of the printed LC3-ECC decrease by 16.6%–22.7 % and 43.3%–54.6 % compared to the cast specimens, while it still possesses a strain capacity of 2 % and multiple cracking behaviour. The compressive and flexural properties of the printed LC3-ECC both show significant anisotropy, the maximum values of which are observed in the Z- and Y- directions, respectively. The interlayer bonding strength increases by 54.3%–91.9 % at 28 d compared to that at 7 d due to the hydration of LC3. The pore structure of the printed LC3-ECC is denser than that of the cast LC3-ECC, with more regular arrangement and finer size of pores. The macro-micro properties correlation analysis proves the better comprehensive performance of printed LC3-ECC relative to casted LC3-ECC, as well as reveals the relationship between pore structure and anisotropy. In terms of material sustainability, 3DP-LC3-ECC has a significant reduction in energy and carbon emissions compared to typical M45-ECC, contributing to the environmental development.

Evidence of Dislocation Mixed Climb in Quartz From the Main Central and Moine Thrusts: An Electron Tomography Study

AbstractIn this study we apply electron tomography to characterize 3D dislocation microstructures in two quartz mylonite specimens from the Moine and Main Central Thrusts, both of which accommodated displacements by dislocation creep in the presence of water. Both specimens show dislocation activity with dislocation densities of the order of 3–4 × 1012 m−2 and evidence of recovery from the presence of subgrain boundaries. 〈a〉 slip occurs predominantly on pyramidal and prismatic planes (〈a〉 basal glide is not active). [c] Glide is not significant. On the other hand, we observe a very high level of activation of 〈c + a〉 glide on the , , (n = 1,2) and even planes. Approximately 60% of all dislocations show evidence of climb with a predominance of mixed climb, a deformation mechanism characterized by dislocations moving in a plane intermediate between the glide and the climb planes. This atypical mode of deformation demonstrates comparable glide and climb efficiency under natural deformation conditions. It promotes dislocation glide in planes not expected for the quartz structure, probably by inhibiting lattice friction. Our quantitative characterization of the microstructure enables us to assess the strain that dislocations can generate. We show that glide systems indicated by the observed dislocations are sufficient to accommodate any arbitrary 3D strain by themselves. Although historically dislocation glide has been regarded as being primarily responsible for producing strain, activation of climb can also directly contribute to the finite strain. On the basis of this characterization, we propose a numerical modeling approach for attempting to characterize the local stress state that gave rise to the observed microstructure.

Study on mechanical properties of natural fiber (Jute)/synthetic fiber (Glass) reinforced polymer hybrid composite by representative volume element using finite element analysis: A numerical approach and validated by experiment

The excellent mechanical strength with low weight of polymer-based composites makes them superior over conventional materials like steel and aluminum in terms of mechanical properties. Nowadays, researchers are working to develop natural fiber-based sustainable composite. Glass fiber, when combined with jute fiber, could greatly enhance the mechanical characteristics of composites; the sequence of the layers mostly determines these properties. In this study, the hybrid laminate of jute and glass fiber with resin polyester is developed with The representative volume element utilized in ANSYS. In ANSYS material design module, the 3D microstructure of composite material is created with various fiber volume fractions of glass fiber and jute fiber. After that, the effective elastic properties of hybrid jute/glass fiber and polyester as matrix, glass fiber/polyester, and jute fiber/polyester are evaluated and the simulation-based effective elastic property is validated by existing experiment value which showed good agreement. For further study, in Ansys ACP PrepPost, the composite laminate such as hybrid jute + glass fiber/polyester, glass fiber/polyester, and jute fiber/polyester is created with desired lamina thickness, stacking sequence, and fiber orientation. Thereafter, it was transferred to a static structure module to perform tensile and bending test analysis. Ansys ACP post-processing is utilized to investigate stress induced at each lamina of the composite specimen. This study revealed that by adding glass fiber to natural fiber, the mechanical performance is enhanced significantly, whereas hybrid jute + glass fiber/polyester (S2) demonstrated the highest tensile strength 259 MPa compared to other hybrid laminate composites. Astonishingly, The hybrid composite jute + glass/polyester (S2) exhibited 10 % higher bending strength than glass fiber/polyester (S1) and 64 % more than net jute fiber/polyester (S5). Additionally, S2 demonstrated 9.5 % higher shear strength compared to S1 and 64 % more than S5. This study will give a great understanding of how adding glass fiber to jute fiber, could enhance the mechanical properties, which will contribute to the design of an efficient composite part for automotive such as car interiors, car doors, trim panels, lightweight applications, and packaging industries.

3D stochastic microstructure reconstruction via slice images and attention-mechanism-based GAN

Stochastic media are used to characterize materials with irregular structure and spatial randomness, and the remarkable macroscopic features of stochastic media are often determined by their internal microstructure. Hardware loads and computational burdens have always been a challenge for the reconstruction of large-volume materials. To tackle the aforementioned concerns, this paper proposes a learning model based on generative adversarial network that uses multiple 2D slice images to reconstruct 3D stochastic microstructures. The whole model training process requires only a 3D image of stochastic media as the training image. In addition, the attention mechanism captures cross-dimensional interactions to prioritize the learned features and improves the effectiveness of training. The model is tested on stochastic porous media with two-phase internal structure and complex morphology. The experimental findings demonstrate that utilizing multiple 2D images helps the model learn better and reduces the occurrence of overfitting, while greatly reducing the hardware loads of the model.

Fabrication of three-dimensional microstructures on GaN surfaces through the integration of femtosecond laser ablation and ICP etching

Gallium nitride (GaN) surface microstructures are crucial for enhancing the performance and reliability of high-power devices. However, the direct fabrication of patterned three-dimensional (3D) microstructures on GaN surfaces remains a significant challenge. This paper introduces an innovative approach, employing a maskless femtosecond laser combined with inductively coupled plasma (ICP) etching, to efficiently fabricate petal-shaped 3D microstructures on GaN surfaces. By fine-tuning the ICP etching and femtosecond laser parameters, we have obtained the regularities of the dimensional variations of these 3D microstructures. Additionally, our investigation reveals that the design of microstructural concavity and convexity characteristics can be flexibly manipulated by controlling the ICP etching rate, which is influenced by the oxide content in the laser-modified zone. This study presents a novel method for straightforward fabrication of microstructures on gallium nitride surfaces.

A facile electrospinning strategy to prepare cost-effective carbon fibers as a self-supporting anode for lithium-ion batteries

Carbon materials with fibrous morphology and enhanced mechanical properties have shown to be promising materials as self-supporting anode materials for lithium-ion batteries (LIBs). In this study, coal-based carbon fibers (CFs) with favorable flexibility and superior tensile strength were prepared from lignite and coal derivatives such as coal-based humic acid and coal tar pitch via electrospinning in the assistance of polyacrylonitrile. All these coal-based CFs have an 1D dense fibrous microstructure with controllable diameter and appreciable specific surface area with enriched in O/N-containing groups by rationally choosing the precursor type. The organic macromolecules enriched aromatic ring in lignite and coal derivatives cross-link with linear polyacrylonitrile molecular chains to form a more solid three-dimensional (3D) network framework under the conditions of electrospinning and carbonization. Such 3D microstructure not only can significantly improve the flexibility and tensile strength of coal-based CFs, but also can increase the content of graphite-like microcrystalline carbon (sp2 carbon) in carbon fibers, thereby improving the electrical conductivity. Due to the smaller molecular structure, coal tar pitch can be more easily combined with polyacrylonitrile by electrostatic force. The CFs derived from coal tar pitch (CTP-CFs) exhibited the largest average diameter (156.2 nm), a higher microporous surface area (2.6 m2·g−1), higher pyrrole nitrogen and contained reasonable proportions of amorphous carbon (sp3 carbon) and sp2 carbon. As a consequence, among the various derived coal-based CFs applied as self-supporting anode materials for LIBs, CTP-CFs showed the highest first reversible capacity (770.8 mAh·g−1 at 20 mA·g−1) and excellent cycling performance with capacity retention rate of 89.1 % after 200 cycles. This work paves a new strategy to prepare CFs as flexible self-supporting anodes for LIBs from low-cost carbon precursors that can be rationally chosen to further tune the property of the CFs.

Multiphase Reconstruction of Heterogeneous Materials Using Machine Learning and Quality of Connection Function.

Establishing accurate structure-property linkages and precise phase volume accuracy in 3D microstructure reconstruction of materials remains challenging, particularly with limited samples. This paper presents an optimized method for reconstructing 3D microstructures of various materials, including isotropic and anisotropic types with two and three phases, using convolutional occupancy networks and point clouds from inner layers of the microstructure. The method emphasizes precise phase representation and compatibility with point cloud data. A stage within the Quality of Connection Function (QCF) repetition loop optimizes the weights of the convolutional occupancy networks model to minimize error between the microstructure's statistical properties and the reconstructive model. This model successfully reconstructs 3D representations from initial 2D serial images. Comparisons with screened Poisson surface reconstruction and local implicit grid methods demonstrate the model's efficacy. The developed model proves suitable for high-quality 3D microstructure reconstruction, aiding in structure-property linkages and finite element analysis.

Emulsion gel-based inks for 3D printing of foods for dysphagia patients: High internal type emulsion gel-biopolymer systems

High internal type emulsion gels (HIPE gels) are usually a soft semi-solid material consisting of tightly packed oil droplets stabilized by a layer of emulsifier. The semi-solid nature of HIPE gels means they can be used as edible inks in 3D food printing applications. However, the rheological properties of HIPE gels are typically unable to satisfy the demands of 3D printing applications, resulting in unsatisfactory printing performance. In this study, HIPE gel-based inks (85% by volume of oil) with improved 3D printing performance were prepared by using pea protein-inulin complexes as emulsifiers. In addition, the effects of oil type (corn, sunflower, flaxseed, or olive oil) on the 3D printing property, rheological properties, and microstructure of the HIPE gels was analyzed since different oils have different nutritional and physicochemical properties. Moreover, International Dysphagia Diet Standardization Initiative (IDDSI) foods created by 3D printing with these edible inks were evaluated in a population of the elderly suffering from dysphagia. It was found that HIPE-based edible inks containing an optimized inulin level (0.75% w/v) had the best 3D printing properties. In terms of oil type, the edible inks formulated from corn oil and from flaxseed oil had the best 3D printing property. As analyzed in the Lissajous plot, the addition of inulin remarkably improved the viscoelasticity of the inks, which improved their 3D printing property. These inks also exhibited high thixotropic recovery and good thermal stability. Moreover, cryo-SEM analysis showed that the PPI-inulin complex formed a barrier on the oil droplets' surface and a dense cross-linked structure between the particles. The IDDSI evaluation found that the HIPE gel inks were able to satisfy the requirements of Class IV and V foods in the IDDSI framework and could be classified as transitional foods, which would satisfy the food needs of patients with dysphagia.

Multiscale topology optimization for the design of spatially-varying three-dimensional lattice structure

This paper introduces three dimensional (3D) topology optimization specifically tailored for designing spatially-varying primitive-cubic (CP) type lattice structures. The developed design process consists of three steps: pre-processing, main processing, and post-processing. In the pre-processing step, a surrogate model between lattice geometry variables and effective elasticity tensor is constructed using an artificial neural network. This surrogate model is essential because it enables the quick calculation of the effective elasticity tensor of lattice microstructure. In the subsequent main processing step, multiscale topology optimization is performed. During this step, lattice microstructure size and rotation design variables are optimized together with the macrostructure density design variables. Macrostructures are designed using a well-established three-field density scheme with a SIMP formulation. Formulations for spatially-varying 3D lattice microstructure are newly developed based on a unit quaternion rotation representation scheme. In the final post-processing step, a spatially-varying microstructure is restored using a de-homogenization scheme, newly developed particularly for the primitive-cubic (CP) type 3D lattice structures. The effectiveness and robustness of the proposed formulations are validated through three design examples for the compliance minimization problem. Moreover, a designed lattice structure is fabricated using an additive manufacturing machine.

A peridynamic differential operator modeling approach for ceramic matrix composites microstructure with uniform or non-uniform discretization

Based on the theory of peridynamic differential operator (PDDO), a novel three-dimensional (3D) peridynamic (PD) model for isotropic and orthotropic material is proposed to investigate the elastic mechanical behaviors and stress distribution of Ceramic Matrix Composites (CMC) microstructure. The non-local expressions of strain and stress tensors are derived by employing PDDO and the classical constitutive equations. The bond forces in interface region crossing two different constituent materials are obtained by converting the partial differential terms into the non-local forms. The weak form of PD equations of motion is derived by using the principle of virtual work and PDDO. The validity of this approach is verified by predicting the elastic response and stress distributions of isotropic and orthotropic materials under uniaxial tension and pure shear deformation. Finally, simulations are conducted on a CMC microstructure with fiber and matrix under periodic boundary conditions. It is demonstrated that the current approach can effectively build 3D CMC microstructure with uniform or non-uniform discretization, and accurately predict the stress fields and effective elastic properties.

Printing Green: Microalgae-Based Materials for 3D Printing with Light.

Microalgae have emerged as sustainable feedstocks due to their ability to fix CO2 during cultivation, rapid growth rates, and capability to produce a wide variety of metabolites. Several microalgae accumulate lipids in high concentrations, especially triglycerides, along with lipid-soluble, photoactive pigments such as chlorophylls and derivatives. Microalgae-derived triglycerides contain longer fatty acid chains with more double bonds on average than vegetable oils, allowing a higher degree of post-functionalization. Consequently, they are especially suitable as precursors for materials that can be used in 3D printing with light. This work presents the use of microalgae as "biofactories" to generate materials that can be further 3D printed in high resolution. Two taxonomically different strains-Odontella aurita (O. aurita, BEA0921B) and Tetraselmis striata (T. striata, BEA1102B)-are identified as suitable microalgae for this purpose. The extracts obtained from the microalgae (mainly triglycerides with chlorophyll derivatives) are functionalized with photopolymerizable groups and used directly as printable materials (inks) without the need for additional photoinitiators. The fabrication of complex 3D microstructures with sub-micron resolution is demonstrated. Notably, the 3D printed materials show biocompatibility. These findings open new possibilities for the next generation of sustainable, biobased, and biocompatible materials with great potential in life science applications.