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Robert W. Epps

Other affiliations: Vanderbilt University
Bio: Robert W. Epps is an academic researcher from North Carolina State University. The author has contributed to research in topics: Quantum dot & Perovskite (structure). The author has an hindex of 8, co-authored 21 publications receiving 310 citations. Previous affiliations of Robert W. Epps include Vanderbilt University.

Papers
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Journal ArticleDOI
TL;DR: An Artificial Chemist is presented: the integration of machine-learning-based experiment selection and high-efficiency autonomous flow chemistry that enhances the optoelectronic properties of the in-flow synthesized QDs and mitigates the issues of batch-to-batch precursor variability.
Abstract: The optimal synthesis of advanced nanomaterials with numerous reaction parameters, stages, and routes, poses one of the most complex challenges of modern colloidal science, and current strategies often fail to meet the demands of these combinatorially large systems. In response, an Artificial Chemist is presented: the integration of machine-learning-based experiment selection and high-efficiency autonomous flow chemistry. With the self-driving Artificial Chemist, made-to-measure inorganic perovskite quantum dots (QDs) in flow are autonomously synthesized, and their quantum yield and composition polydispersity at target bandgaps, spanning 1.9 to 2.9 eV, are simultaneously tuned. Utilizing the Artificial Chemist, eleven precision-tailored QD synthesis compositions are obtained without any prior knowledge, within 30 h, using less than 210 mL of total starting QD solutions, and without user selection of experiments. Using the knowledge generated from these studies, the Artificial Chemist is pre-trained to use a new batch of precursors and further accelerate the synthetic path discovery of QD compositions, by at least twofold. The knowledge-transfer strategy further enhances the optoelectronic properties of the in-flow synthesized QDs (within the same resources as the no-prior-knowledge experiments) and mitigates the issues of batch-to-batch precursor variability, resulting in QDs averaging within 1 meV from their target peak emission energy.

164 citations

Journal ArticleDOI
TL;DR: This work comprehensively characterize nanocrystal growth within a modular microfluidic reactor and enables a systematic study of the effect of mixing enhancement on the quality of the synthesized nanocrystals through a direct comparison between single- and multi-phase flow systems at similar reaction time scales.
Abstract: Colloidal organic/inorganic metal-halide perovskite nanocrystals have recently emerged as a potential low-cost replacement for the semiconductor materials in commercial photovoltaics and light emitting diodes. However, unlike III–V and IV–VI semiconductor nanocrystals, studies of colloidal perovskite nanocrystals have yet to develop a fundamental and comprehensive understanding of nucleation and growth kinetics. Here, we introduce a modular and automated microfluidic platform for the systematic studies of room-temperature synthesized cesium–lead halide perovskite nanocrystals. With abundant data collection across the entirety of four orders of magnitude reaction time span, we comprehensively characterize nanocrystal growth within a modular microfluidic reactor. The developed high-throughput screening platform features a custom-designed three-port flow cell with translational capability for in situ spectral characterization of the in-flow synthesized perovskite nanocrystals along a tubular microreactor with an adjustable length, ranging from 3 cm to 196 cm. The translational flow cell allows for sampling of twenty unique residence times at a single equilibrated flow rate. The developed technique requires an average total liquid consumption of 20 μL per spectra and as little as 2 μL at the time of sampling. It may continuously sample up to 30 000 unique spectra per day in both single and multi-phase flow formats. Using the developed plug-and-play microfluidic platform, we study the growth of cesium lead trihalide perovskite nanocrystals through in situ monitoring of their absorption and emission band-gaps at residence times ranging from 100 ms to 17 min. The automated microfluidic platform enables a systematic study of the effect of mixing enhancement on the quality of the synthesized nanocrystals through a direct comparison between single- and multi-phase flow systems at similar reaction time scales. The improved mixing characteristics of the multi-phase flow format results in high-quality perovskite nanocrystals with kinetically tunable emission wavelength, ranging as much as 25 nm at equivalent residence times. Further application of this unique platform would allow rapid parameter optimization in the colloidal synthesis of a wide range of nanomaterials (e.g., metal or semiconductor), that is directly transferable to continuous manufacturing in a numbered-up platform with a similar characteristic length scale.

108 citations

Journal ArticleDOI
TL;DR: This progress report details the basic principles of microfluidic reactor design and performance, as well as the current state of online diagnostics and autonomous robotic experimentation strategies, toward the size, shape, and composition-controlled synthesis of various colloidal nanomaterials.
Abstract: In recent years, microfluidic technologies have emerged as a powerful approach for the advanced synthesis and rapid optimization of various solution-processed nanomaterials, including semiconductor quantum dots and nanoplatelets, and metal plasmonic and reticular framework nanoparticles. These fluidic systems offer access to previously unattainable measurements and synthesis conditions at unparalleled efficiencies and sampling rates. Despite these advantages, microfluidic systems have yet to be extensively adopted by the colloidal nanomaterial community. To help bridge the gap, this progress report details the basic principles of microfluidic reactor design and performance, as well as the current state of online diagnostics and autonomous robotic experimentation strategies, toward the size, shape, and composition-controlled synthesis of various colloidal nanomaterials. By discussing the application of fluidic platforms in recent high-priority colloidal nanomaterial studies and their potential for integration with rapidly emerging artificial intelligence-based decision-making strategies, this report seeks to encourage interdisciplinary collaborations between microfluidic reactor engineers and colloidal nanomaterial chemists. Full convergence of these two research efforts offers significantly expedited and enhanced nanomaterial discovery, optimization, and manufacturing.

57 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used scanning electron microscopy to evaluate the porosity patterns found in microconcrete materials to better understand the gaseous diffusion pathways and reaction of CO2 within the bulk cement paste and interfacial transition zone (ITZ) of microcrete materials containing different fly ash replacement types.

42 citations


Cited by
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Journal Article
TL;DR: In this article, the authors used in situ transmission electron microscopy to show that platinum nanocrystals can grow either by monomer attachment from solution onto the existing particles or by coalescence between the particles.
Abstract: It is conventionally assumed that the growth of monodisperse colloidal nanocrystals requires a temporally discrete nucleation followed by monomer attachment onto the existing nuclei. However, recent studies have reported violations of this classical growth model, and have suggested that inter-particle interactions are also involved during the growth. Mechanisms of nanocrystal growth still remain controversial. Using in situ transmission electron microscopy, we show that platinum nanocrystals can grow either by monomer attachment from solution onto the existing particles or by coalescence between the particles. Surprisingly, an initially broad size distribution of the nanocrystals can spontaneously narrow. We suggest that nanocrystals take different pathways of growth based on their size- and morphology-dependent internal energies. These observations are expected to be highly relevant for other nanocrystal systems.

949 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide an overview of the synthesis, microstructure variation, luminescence properties and potential applications of hybrid organic and all-inorganic perovskites.
Abstract: In the past few years, metal halide perovskite quantum dots and nanocrystals have been extensively explored due to their unique optoelectronic properties and extensive application prospects. In this review article, we provide an overview of the synthesis, microstructure variation, luminescence properties and potential applications of hybrid organic–inorganic perovskites and all-inorganic perovskites. Firstly, several widely used wet chemical methods for synthesizing perovskite quantum dots are summarized, and then the related structures and morphologies of different perovskites as well as the relationship between the microstructure and optical properties are given. Additionally, correlative metal halides with B-site ion replacement or doping and some strategies to improve the stability of perovskite materials are highlighted. Subsequently, a brief introduction about their potential applications in light-emitting diodes, photodetectors and lasing is presented. Finally, some conclusions and outlooks will be introduced. It is expected that this review article will provide valuable insights into the current status of perovskite luminescent materials and stimulate new ideas and further research on their practical applications.

174 citations

Journal ArticleDOI
TL;DR: An Artificial Chemist is presented: the integration of machine-learning-based experiment selection and high-efficiency autonomous flow chemistry that enhances the optoelectronic properties of the in-flow synthesized QDs and mitigates the issues of batch-to-batch precursor variability.
Abstract: The optimal synthesis of advanced nanomaterials with numerous reaction parameters, stages, and routes, poses one of the most complex challenges of modern colloidal science, and current strategies often fail to meet the demands of these combinatorially large systems. In response, an Artificial Chemist is presented: the integration of machine-learning-based experiment selection and high-efficiency autonomous flow chemistry. With the self-driving Artificial Chemist, made-to-measure inorganic perovskite quantum dots (QDs) in flow are autonomously synthesized, and their quantum yield and composition polydispersity at target bandgaps, spanning 1.9 to 2.9 eV, are simultaneously tuned. Utilizing the Artificial Chemist, eleven precision-tailored QD synthesis compositions are obtained without any prior knowledge, within 30 h, using less than 210 mL of total starting QD solutions, and without user selection of experiments. Using the knowledge generated from these studies, the Artificial Chemist is pre-trained to use a new batch of precursors and further accelerate the synthetic path discovery of QD compositions, by at least twofold. The knowledge-transfer strategy further enhances the optoelectronic properties of the in-flow synthesized QDs (within the same resources as the no-prior-knowledge experiments) and mitigates the issues of batch-to-batch precursor variability, resulting in QDs averaging within 1 meV from their target peak emission energy.

164 citations

Journal ArticleDOI
TL;DR: The rapid production (and analysis) of droplets allows for exceptionally high-throughput experimentation and data acquisition, and configurable channel designs, coupled with on-demand control architectures, engender a range of robust manipulations.
Abstract: Microfluidic platforms have changed the paradigm of biochemical experimentation over the past three decades. Prominent within this technology set are droplet-based microfluidic systems, in which passive microfluidic structures are used to rapidly generate and manipulate sub-nanoliter volumes droplets within microchannel environments. Droplets are formed in a continuous and robust fashion through the extrusion and shearing of two mutually immiscible phases in a microchannel, with droplet volumes being precisely controlled through the variation of flow rate ratios and channel dimensions. The rapid production (and analysis) of droplets allows for exceptionally high-throughput experimentation and data acquisition, and configurable channel designs, coupled with on-demand control architectures, engender a range of robust manipulations, such as reagent dosing, droplet fusion, droplet splitting, washing, payload heating, incubation, content dilution and droplet sorting. Accordingly, and unsurprisingly, droplet-based microfluidic systems have become an indispensable and embedded tool within contemporary chemical and biological science.

161 citations

Journal ArticleDOI
TL;DR: Some of the chief advancements of these methods and their applications in rational materials design are reviewed, followed by a discussion on some of the main challenges and opportunities the authors currently face together with a perspective on the future ofrational materials design and discovery.
Abstract: Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.

145 citations