Real-time autonomous control of a continuous macroscopic process as demonstrated by plastic forming.

To meet the need for more adaptable and expedient approaches in research and manufacturing, we present a continuous autonomous system that leverages real-time, characterization and an active-learning-based decision-making processor. This system was applied to a plastic film forming process to demonstrate its capability in autonomously determining process conditions for specified film dimensions without human intervention. Application of the system to nine film dimensions (width and thickness) highlighted its ability to explore the search space and identify appropriate and stable process conditions, with an average of 11 characterization-adjustment iterations and a processing time of 19 minutes per width, thickness combination.

Tabular Two-Dimensional Correlation Analysis for Multifaceted Characterization Data.

We propose tabular two-dimensional correlation spectroscopy analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to data sets of carbon nanotube (CNT) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements such as voids, bundles, and amorphous carbon.

Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning.

Synthetic trade-offs exist in the synthesis of single-walled carbon nanotube (SWCNT) forests, as growing certain desired properties can often come at the expense of other desirable characteristics such as the case of crystallinity and growth efficiency. Simultaneously achieving mutually exclusive properties in the growth of SWCNT forests is a significant accomplishment, as it requires overcoming these trade-offs and balancing competing mechanisms. To address this, we trained a machine-learning regression model with a set of 585 "real" experimental synthesis data, which were taken using an automatic synthesis reactor.

A Comprehensive and Versatile Multimodal Deep-Learning Approach for Predicting Diverse Properties of Advanced Materials.

A multimodal deep-learning (MDL) framework is presented for predicting physical properties of a ten-dimensional acrylic polymer composite material by merging physical attributes and chemical data. The MDL model comprises four modules, including three generative deep-learning models for material structure characterization and a fourth model for property prediction. The approach handles an 18-dimensional complexity, with ten compositional inputs and eight property outputs, successfully predicting 913 680 property data points across 114 210 composition conditions.

A Microwave-Assisted, Solvent-Free Approach for the Versatile Functionalization of Carbon Nanotubes.

While the functionalization of carbon nanotubes (CNTs) has attracted extensive interest for a wide range of applications, a facial and versatile strategy remains in demand. Here, we report a microwave-assisted, solvent-free approach to directly functionalize CNTs both in raw form and in arbitrary macroscopic assemblies. Rapid microwave irradiation was applied to generate active sites on the CNTs while not inducing excessive damage to the graphitic network, and a gas-phase deposition afforded controllable grafting for thorough or regioselective functionalization.

Liquid Crystalline Behaviors of Single-Walled Carbon Nanotubes in an Aqueous Sodium Cholate Dispersion.

Controlling the alignment of single-walled carbon nanotubes (SWCNTs) on the macroscopic scale is critical for practical applications because SWCNTs are extremely anisotropic materials. One efficient technique is to create an effective SWCNT dispersion, which shows a liquid crystal (LC) phase. A strong acid treatment can realize SWCNT liquid crystalline dispersions.

Comprehensive Characterization of Structural, Electrical, and Mechanical Properties of Carbon Nanotube Yarns Produced by Various Spinning Methods.

A comprehensive characterization of various carbon nanotube (CNT) yarns provides insight for producing high-performance CNT yarns as well as a useful guide to select the proper yarn for a specific application. Herein we systematically investigate the correlations between the physical properties of six CNT yarns produced by three spinning methods, and their structures and the properties of the constituent CNTs. The electrical conductivity increases in all yarns regardless of the spinning method as the effective length of the constituent CNTs and the density of the yarns increase.

Novel Approaches to In-Situ ATR-FTIR Spectroscopy and Spectroscopic Imaging for Real-Time Simultaneous Monitoring Curing Reaction and Diffusion of the Curing Agent at Rubber Nanocomposite Surface.

Here, we propose a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy method for simultaneously monitoring the curing reaction and the diffusion behavior of curing agents at the surface of rubber in real-time. The proposed scheme was demonstrated by fluorine rubber (FKM) and FKM/carbon nanotube (CNT) nanocomposites with a target curing agent of triallyl-isocyanurate (TAIC). The broadening and the evolution of the C=O stretching of TAIC were quantitatively analyzed to characterize the reaction and the diffusion.

N Gas Adsorption Sites of Single-Walled Carbon Nanotube Bundles: Identifying Interstitial Channels at Very Low Relative Pressure.

Utilizing the nanoscale space created by carbon nanotubes (CNTs) is of importance for applications like energy storage devices, sensors, and functional materials. Gas adsorption is a versatile, quantitative characterization method to analyze nanoscale pore sizes and volumes. Here, we inspected N adsorption to the nanospace formed by the bundles of single-walled CNTs with an average nanotube diameter of ca.

Near-Infrared Hyperspectral Absorption/Scattering Imaging Method Using Multiple Ground Plates for Evaluating Polymer Composites.

This paper proposes a nondestructive method of evaluating polymer composites using near-infrared (NIR) diffuse reflection spectroscopy with multiple ground plates. Wavelength-dependent absorption and reduced scattering coefficients were acquired to evaluate the chemical structure and the concentration of the substances from absorption and to determine the size and the dispersity of filler in the polymer domain from scattering. NIR spectra of the sample were measured on multiple ground plates, namely, "ground-plate-dependent" diffuse reflection spectra.

Near-Infrared Spectroscopic Evaluation of the Water Content of Molded Polylactide under the Effect of Crystallization.

During melt processing, the moisture inside polylactide (PLA) easily induces hydrolysis, which deteriorates the mechanical and thermal properties of the product. The state of dryness of resin pellets must be monitored to prevent PLA hydrolysis. In this study, near-infrared (NIR) spectroscopy was applied to measure water content in PLA.