Robofurnace: a semi-automated laboratory chemical vapor deposition system for high-throughput nanomaterial synthesis and process discovery

Multiscale Plasmonic Refractory Nanocomposites for High-Temperature Solar Photothermal Conversion

Development of a refractory selective solar absorber (RSSA) is the key to unlock high-temperature solar thermal and thermochemical conversion. The fundamental challenge of RSSA is the lack of design and fabrication guidelines to simultaneously achieve omnidirectional, broadband solar absorption and sharp spectral selectivity at the desired cutoff wavelength. Here, we realize a ruthenium-carbon nanotube (Ru-CNT) nanocomposite RSSA with multiscale nanoparticle-on-nanocavity plasmonic modes. Ru conformally coated on the sidewalls of CNTs enables a spoof surface plasmon polariton mode for spectra selectivity; Ru nanoparticles formed at the tips of CNTs enable a localized surface plasmon resonance mode and plasmon hybridization for omnidirectional broadband solar absorption. The fabricated Ru-CNT RSSA has a total solar absorption (TSA) of 96.1% with sharp spectral cutoff at 2.21 μm. The TSA is maintained at over 90% for an incident angle of 56°. Our findings therefore guide full-spectrum optical and thermal control from visible to the far-infrared.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.

Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high-throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high-throughput techniques. For intuitive visualization, a graphical phase-property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning-based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.

Anisotropic Carbon Nanotube Structures with High Aspect Ratio Nanopores for Li-Ion Battery Anodes

Technological advances in membrane technology, catalysis, and electrochemical energy storage require the fabrication of controlled pore structures at ever smaller length scales. It is therefore important to develop processes allowing for the fabrication of materials with controlled submicron porous structures. We propose a combination of colloidal lithography and chemical vapor deposition of carbon nanotubes to create continuous straight pores with diameters down to 100 nm in structures with thicknesses of more than 300 μm. These structures offer unique features, including continuous and parallel pores with aspect ratios in excess of 3000, a low pore tortuosity, good electrical conductivity, and electrochemical stability. We demonstrate that these structures can be used in Li-ion batteries by coating the carbon nanotubes with Si as an active anode material.

Carbon Nanotubes and Related Nanomaterials: Critical Advances and Challenges for Synthesis toward Mainstream Commercial Applications

Advances in the synthesis and scalable manufacturing of single-walled carbon nanotubes (SWCNTs) remain critical to realizing many important commercial applications. Here we review recent breakthroughs in the synthesis of SWCNTs and highlight key ongoing research areas and challenges. A few key applications that capitalize on the properties of SWCNTs are also reviewed with respect to the recent synthesis breakthroughs and ways in which synthesis science can enable advances in these applications. While the primary focus of this review is on the science framework of SWCNT growth, we draw connections to mechanisms underlying the synthesis of other 1D and 2D materials such as boron nitride nanotubes and graphene.

Oxygen-promoted catalyst sintering influences number density, alignment, and wall number of vertically aligned carbon nanotubes

A lack of synthetic control and reproducibility during vertically aligned carbon nanotube (CNT) synthesis has stifled many promising applications of organic nanomaterials. Oxygen-containing species are particularly precarious in that they have both beneficial and deleterious effects and are notoriously difficult to control. Here, we demonstrated diatomic oxygen's ability, independent of water, to tune oxide-supported catalyst thin film dewetting and influence nanoscale (diameter and wall number) and macro-scale (alignment and density) properties for as-grown vertically aligned CNTs. In particular, single- or few-walled CNT forests were achieved at very low oxygen loading, with single-to-multi-walled CNT diameters ranging from 4.8 ± 1.3 nm to 6.4 ± 1.1 nm over 0-800 ppm O₂, and an expected variation in alignment, where both were related to the annealed catalyst morphology. Morphological differences were not the result of subsurface diffusion, but instead occurred via Ostwald ripening under several hundred ppm O₂, and this effect was mitigated by high H₂ concentrations and not due to water vapor (as confirmed in O₂-free water addition experiments), supporting the importance of O₂ specifically. Further characterization of the interface between the Fe catalyst and Al₂O₃ support revealed that either oxygen-deficit metal oxide or oxygen-adsorption on metals could be functional mechanisms for the observed catalyst nanoparticle evolution. Taken as a whole, our results suggest that the impacts of O₂ and H₂ on the catalyst evolution have been underappreciated and underleveraged in CNT synthesis, and these could present a route toward facile manipulation of CNT forest morphology through control of the reactive gaseous atmosphere alone.

Discovery of wall-selective carbon nanotube growth conditions via automated experimentation

Applications of carbon nanotubes continue to advance, with substantial progress in nanotube electronics, conductive wires, and transparent conductors to name a few. However, wider application remains impeded by a lack of control over production of nanotubes with the desired purity, perfection, chirality, and number of walls. This is partly due to the fact that growth experiments are time-consuming, taking about 1 day per run, thus making it challenging to adequately explore the many parameters involved in growth. We endeavored to speed up the research process by automating CVD growth experimentation. The adaptive rapid experimentation and in situ spectroscopy CVD system described in this contribution conducts over 100 experiments in a single day, with automated control and in situ Raman characterization. Linear regression modeling was used to map regions of selectivity toward single-wall and multiwall carbon nanotube growth in the complex parameter space of the water-assisted CVD synthesis. This development of the automated rapid serial experimentation is a significant progress toward an autonomous closed-loop learning system: a Robot Scientist.

Robofurnace: a semi-automated laboratory chemical vapor deposition system for high-throughput nanomaterial synthesis and process discovery

Laboratory research and development on new materials, such as nanostructured thin films, often utilizes manual equipment such as tube furnaces due to its relatively low cost and ease of setup. However, these systems can be prone to inconsistent outcomes due to variations in standard operating procedures and limitations in performance such as heating and cooling rates restrict the parameter space that can be explored. Perhaps more importantly, maximization of research throughput and the successful and efficient translation of materials processing knowledge to production-scale systems, relies on the attainment of consistent outcomes. In response to this need, we present a semi-automated lab-scale chemical vapor deposition (CVD) furnace system, called "Robofurnace." Robofurnace is an automated CVD system built around a standard tube furnace, which automates sample insertion and removal and uses motion of the furnace to achieve rapid heating and cooling. The system has a 10-sample magazine and motorized transfer arm, which isolates the samples from the lab atmosphere and enables highly repeatable placement of the sample within the tube. The system is designed to enable continuous operation of the CVD reactor, with asynchronous loading∕unloading of samples. To demonstrate its performance, Robofurnace is used to develop a rapid CVD recipe for carbon nanotube (CNT) forest growth, achieving a 10-fold improvement in CNT forest mass density compared to a benchmark recipe using a manual tube furnace. In the long run, multiple systems like Robofurnace may be linked to share data among laboratories by methods such as Twitter. Our hope is Robofurnace and like automation will enable machine learning to optimize and discover relationships in complex material synthesis processes.