US-12618761-B2 - High-throughput quantitative measurement of bulk mechanical properties of soft materials

Abstract

Provided herein are systems and methods for carrying out high-throughput quantitative measurements of bulk mechanical properties of soft materials. The systems include a centrifuge, solid particles, and sample wells. In the systems and methods, samples comprising solid particles embedded in soft materials contained within sample wells are centrifuged in a series of increasing centrifugal speed increments, and a bulk mechanical property, such as fracture stress or elastic modulus, of each soft material is determined by monitoring the centrifugal force needed for the solid particles to fracture the soft material in each of the samples.

Inventors

• Muzhou Wang
• Justin E. Griffith
• Yusu Chen
• Kenneth R. Shull
• Danielle Tullman-Ercek

Assignees

• NORTHWESTERN UNIVERSITY

Dates

Publication Date

20260505

Application Date

20230607

Claims (13)

1. 1 . A method for measuring a bulk mechanical property of one or more soft materials, the method comprising: preparing one or more soft material samples in one or more sample wells, the one or more soft material samples comprising one or more solid particles embedded in a soft material; mounting the one or more sample wells in a centrifuge in an inverted configuration; spinning the one or more sample wells in the centrifuge in a series of increasing centrifugal velocity increments; and monitoring the one or more soft material samples after each centrifugal velocity increment to determine whether the one or more solid particles have broken through the soft material in the one or more soft material samples.
2. 2 . The method of claim 1 , wherein the bulk mechanical property is fracture stress.
3. 3 . The method of claim 1 , wherein the bulk mechanical property is elastic modulus.
4. 4 . The method of claim 1 , wherein more than one of the soft material samples are prepared and monitored.
5. 5 . The method of claim 4 , wherein at least 100 soft material samples are prepared and monitored.
6. 6 . The method of claim 4 , wherein different soft material samples comprise different soft materials.
7. 7 . The method of claim 1 , wherein the soft material of one or more of the soft material samples comprises an organic polymer.
8. 8 . The method of claim 7 , wherein the soft material of one or more of the soft material samples comprises an organogel.
9. 9 . The method of claim 7 , wherein the soft material of one or more of the soft material samples comprises a hydrogel.
10. 10 . The method of claim 7 , wherein the soft material of one or more of the soft material samples comprises an elastomer.
11. 11 . The method of claim 1 , wherein preparing the one or more soft material samples comprises placing the one or more solid particles into the one or more sample wells, adding a liquid comprising the soft material or comprising a precursor of the soft material into the one or more sample wells, and solidifying the one or more soft materials within the one or more sample wells.
12. 12 . The method of claim 1 , wherein monitoring the one or more soft material samples after each centrifugal velocity increment comprises photographing the one or more soft material samples after each centrifugal speed increment.
13. 13 . The method of claim 1 , wherein the method is carried out until the one or more solid particles have broken through the soft material in each of the one or more soft material samples.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present application claims priority to U.S. provisional patent application no. 63/349,643 that was filed Jun. 7, 2022, the entire disclosure of which is incorporated herein by reference. REFERENCE TO GOVERNMENT RIGHTS This invention was made with government support under grant number DMR1720139 awarded by the National Science Foundation. The government has certain rights in the invention. The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. BACKGROUND In the era of Big Data and Artificial Intelligence (AI), there is a pressing demand for large datasets of materials properties to train machine learning models that can aid in materials discovery. While advances in combinatorial chemistry have greatly accelerated the synthesis of novel materials, characterization methods have not kept up with this increased throughput, creating a bottleneck in materials development. To address these issues, high-throughput characterization methods have previously measured properties such as melting temperature, oxidation behavior, and electrical conductivity. However, measuring mechanical properties at high throughput is significantly more challenging, as existing methods are often limited by slow, complex fabrication steps or expensive and inaccessible custom instrumentation. Many such techniques already exist, including scanning nanoindentation, micromachined cantilever beams, microelectromechanical systems, microtensile testing, and microrheology. These methods can measure properties such as elastic modulus, hardness, thin film thermomechanical behavior, and ultimate tensile strength. For example, scanning nanoindentation can measure hardness and elastic modulus in a high-throughput manner, but it requires expensive and uncommon instrumentation. Microrheology has also been utilized to measure elastic modulus and viscosity at high throughput, but it requires complex custom instrumentation and is optimized for materials with a very low modulus or viscosity. Parallel microtensile testing can characterize mechanical properties in a high-throughput manner, but samples must be fabricated into a specific form and aligned precisely, limiting the overall throughput of the method. Moreover, many common experimental approaches rely on robotic measurement, but not all materials scientists have access to large capital equipment budgets or in-house automation support teams. A simple and widely-accessible high-throughput mechanical test that can quantitatively measure properties could significantly speed up the discovery and development of novel materials, enabling the masses to contribute to the new revolution in AI-based materials design. Centrifugation is another technique that has been used for high-throughput testing via single-particle force spectroscopy, and more recently, in adhesive strength measurements of soft materials. (Y. Chen et al., ACS Cent. Sci., 2021, 7, 1135.) Soft materials present an important target for high-throughput mechanical characterization. The mechanical properties of such materials are highly tunable, especially by adding extra components to form composites with enhanced properties. However, the presence of these additives significantly increases the number of possible formulations. More testing is required to fully explore the soft materials library. Sequence-specific polymers are another interesting class of soft materials that allow precise control of material properties by directly tailoring monomeric sequences. (A. J. DeStefano et al., JACS Au, 2021,1,155 6.) Fully unlocking the potential of these materials, however, requires a deep understanding of the sequence-structure-function paradigm, which again necessitates large-scale experimentation because the sequence space is exponentially large. (DeStefano et al., 2021.) Fortunately, the preparation of many soft materials is simple enough that samples across a wide range of compositions can be synthesized in a high-throughput manner. For example, automated pipetting systems have been shown to be effective at creating various hydrogels at high throughput. (Y. Ding et al., Adv. Funct. Mater., 2021, 31, 2100489; F. Xu et al., Biomacromolecules, 2020, 21, 214.) This synthesis technique takes advantage of the fact that post-treatment is often not necessary for soft materials, so gels of different compositions can quickly be fabricated by simply combining reactants in varying amounts. This is far simpler than the formation of composition gradients for hard materials such as alloys, which often requires very slow diffusion processing. Unfortunately, high-throughput studies of soft materials are frequently bottlenecked by the mechanical characterization step, which is often still performed with standard one-at-a-time testing. More accessibl