Novel 3D Printing Method

New direction,new materials.

Novel 3D printing method a 'game changer' for discovery, manufacturing of new materials  by Karla Cruise, University of Notre Dame

The design strategy of HTCP. a, Schematic illustration of the combinatorial printing method based on in situ aerosol mixing. b, Orthogonal and parallel gradient printing design strategies, and corresponding printed gradient patterns using blue ink (food dye Blue 1) and red ink (rhodamine B), demonstrating a compositional–modulation feature. c, Optical microscopy images showing the impact of aerosol ink flow rate on the deposited materials. Scale bar, 100 µm. d, Printed material thickness versus flow rate of various inks (polystyrene, AgNW, graphene and Bi2Te3). Error bars represent s.d. from four experimental replicates. sccm, standard cubic centimeters per minute. Credit: Nature (2023). DOI: 10.1038/s41586-023-05898-9

The time-honored Edisonian trial-and-error process of discovery is slow and labor-intensive. This hampers the development of urgently needed new technologies for clean energy and environmental sustainability, as well as for electronics and biomedical devices.

"It usually takes 10 to 20 years to discover a new material," said Yanliang Zhang, associate professor of aerospace and mechanical engineering at the University of Notre Dame.

"I thought if we could shorten that time to less than a year—or even a few months—it would be a game changer for the discovery and manufacturing of new materials."

Now Zhang has done just that, creating a novel 3D printing method that produces materials in ways that conventional manufacturing can't match. The new process mixes multiple aerosolized nanomaterial inks in a single printing nozzle, varying the ink mixing ratio on the fly during the printing process. This method—called high-throughput combinatorial printing (HTCP)—controls both the printed materials' 3D architectures and local compositions and produces materials with gradient compositions and properties at microscale spatial resolution.

His research was just published in Nature.

The aerosol-based HTCP is extremely versatile and applicable to a broad range of metals, semiconductors and dielectrics, as well as polymers and biomaterials. It generates combinational materials that function as "libraries," each containing thousands of unique compositions.

Combining combinational materials printing and high-throughput characterization can significantly accelerate materials discovery, Zhang said. His team has already used this approach to identify a semiconductor material with superior thermoelectric properties, a promising discovery for energy harvesting and cooling applications.

In addition to speeding up discovery, HTCP produces functionally graded materials that gradually transition from stiff to soft. This makes them particularly useful in biomedical applications that need to bridge between soft body tissues and stiff wearable and implantable devices

In the next phase of research, Zhang and the students in his Advanced Manufacturing and Energy Lab plan to apply machine learning and artificial intelligence-guided strategies to the data-rich nature of HTCP in order to accelerate the discovery and development of a broad range of materials.  ... ' 

More Details, but Not Enough

Interesting point, but do not fully understand.    Posting to be revisited.

More Details, but Not Enough

By Gregory Goth, Commissioned by CACM Staff, March 29, 2023

Nearly two years since the publication of the paper in Nature, Google has not yet fully open-sourced the data or code on which its claims were based.

The contentious discussion over the validity of Google researchers' claim that machine learning agents could achieve superhuman results in creating plans for computer chips entered a new, more public phase Tuesday (March 28), with a leading researcher in design automation finding the Google technology did not perform as its authors claimed in a paper published nearly two years ago in Nature.

The dispute around the Nature paper's claims has bubbled for nearly a year in prepared public statements and GitHub code repositories and FAQ sections; researchers directly involved in the situation have declined to speak extemporaneously for the public record. Even some subject matter experts have not wished to speak openly, given Google's dominant position in its ability to distribute research resources to academic computer scientists. However, Tuesday's presentation by Andrew Kahng, a prominent University of California, San Diego researcher in the field of electronic design automation (EDA), at the 2023 ACM/IEEE International Symposium on Physical Design, could elevate the issue to a more open avenue of argument among industry and academic experts.

Briefly stated, the authors of the Nature paper claimed their reinforcement learning (RL) agents could revolutionize the labor-intensive task of floorplanning—the architecting of the incredibly intricate network of memory components (called macro blocks) and logic circuitry (standard cells) on a chip. "Our method generates manufacturable chip floorplans in under six hours, compared to the strongest baseline, which requires months of intense effort by human experts," the authors wrote.

Kahng served as a peer reviewer for the paper, and also wrote an encapsulation for the news and views section of the journal, quoting science fiction author Arthur C. Clarke's observation that any sufficiently advanced technology is indistinguishable from magic.

"To long-time practitioners in the fields of chip design and design automation, (lead author Azalia) Mirhoseini and colleagues' results can indeed seem magical," Kahng wrote.

How open is open?

Science is not magic, however, and the Google paper's claims took the research community by storm. At the conclusion of his summation, Kahng wrote, "We can therefore expect the semiconductor industry to redouble its interest in replicating the authors' work, and to pursue a host of similar applications throughout the chip-design process."

For researchers who presumably were interested in trying to replicate those results, the Google team noted at the end of the paper that "the data supporting the findings of this study are available within the paper and the Extended Data," and that "the code used to generate these data is available from the corresponding authors upon reasonable request."

Are We Getting Closer to Artificial Life?

Implications for Health Care?  Health-Technical. 

In Nature:  Article, Published: 14 September 2022

Living material assembly of bacteriogenic protocells

Can Xu, Nicolas Martin, Mei Li & Stephen Mann 

Nature volume 609, pages 1029–1037 (2022)   Cite this article

Abstract

Advancing the spontaneous bottom-up construction of artificial cells with high organizational complexity and diverse functionality remains an unresolved issue at the interface between living and non-living matter1,2,3,4. Here, to address this challenge, we developed a living material assembly process based on the capture and on-site processing of spatially segregated bacterial colonies within individual coacervate microdroplets for the endogenous construction of membrane-bounded, molecularly crowded, and compositionally, structurally and morphologically complex synthetic cells. The bacteriogenic protocells inherit diverse biological components, exhibit multifunctional cytomimetic properties and can be endogenously remodelled to include a spatially partitioned DNA–histone nucleus-like condensate, membranized water vacuoles and a three-dimensional network of F-actin proto-cytoskeletal filaments. The ensemble is biochemically energized by ATP production derived from implanted live Escherichia coli cells to produce a cellular bionic system with amoeba-like external morphology and integrated life-like properties. Our results demonstrate a bacteriogenic strategy for the bottom-up construction of functional protoliving microdevices and provide opportunities for the fabrication of new synthetic cell modules and augmented living/synthetic cell constructs with potential applications in engineered synthetic biology and biotechnology.  ... '