Lead halide perovskite (LHP) nanocrystals (NCs) are considered an emerging class of advanced functional materials with numerous outstanding optoelectronic characteristics. Despite their success in the field, their precision synthesis and fundamental mechanistic studies remain a challenge. The vast colloidal synthesis and processing parameters of LHP NCs in combination with the batch‐to‐batch and lab‐to‐lab variation problems further complicate their progress. In response, a self‐driving fluidic micro‐processor is presented for accelerated navigation through the complex synthesis and processing parameter space of NCs with multistage chemistries. The capability of the developed autonomous experimentation strategy is demonstrated for a time‐, material‐, and labor‐efficient search through the sequential halide exchange and cation doping reactions of LHP NCs. Next, a machine learning model of the modular fluidic micro‐processors is autonomously built for accelerated fundamental studies of the in‐flow metal cation doping of LHP NCs. The surrogate model of the sequential halide exchange and cation doping reactions of LHP NCs is then utilized for five closed‐loop synthesis campaigns with different target NC doping levels. The precise and intelligent NC synthesis and processing strategy, presented herein, can be further applied toward the autonomous discovery and development of novel impurity‐doped NCs with applications in next‐generation energy technologies.
- Award ID(s):
- 1825356
- NSF-PAR ID:
- 10380385
- Date Published:
- Journal Name:
- Nanoscale
- Volume:
- 13
- Issue:
- 40
- ISSN:
- 2040-3364
- Page Range / eLocation ID:
- 17028 to 17039
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
more » « less
Identifying the optimal formulation of emerging inorganic lead halide perovskite quantum dots (LHP QDs) with their vast colloidal synthesis universe and multiple synthesis/postsynthesis processing parameters is a challenging undertaking for material‐ and time‐intensive, batch synthesis strategies. Herein, a modular microfluidic synthesis strategy, integrated with an artificial intelligence (AI)‐guided decision‐making agent for intelligent navigation through the complex colloidal synthesis universe of LHP QDs with 10 individually controlled synthesis parameters and an accessible parameter space exceeding 2 × 107, is introduced. Utilizing the developed autonomous microfluidic experimentation strategy within a global learning framework, the optimal formulation of LHP QDs is rapidly identified through a two‐step colloidal synthesis and postsynthesis halide exchange reaction, for 10 different emission colors in less than 40 min per desired peak emission energy. Using two in‐series microfluidic reactors enables continuous bandgap engineering of LHP QDs via in‐line halide exchange reactions without the need for an intermediate washing step. Using an inert gas within a three‐phase flow format enables successful, self‐synchronized continuous delivery of halide salt precursor into moving droplets containing LHP QDs, resulting in accelerated closed‐loop formulation optimization and end‐to‐end continuous manufacturing of LHP QDs with desired optoelectronic properties.
-
Multimetallic nanoclusters (MMNCs) offer unique and tailorable surface chemistries that hold great potential for numerous catalytic applications. The efficient exploration of this vast chemical space necessitates an accelerated discovery pipeline that supersedes traditional “trial-and-error” experimentation while guaranteeing uniform microstructures despite compositional complexity. Herein, we report the high-throughput synthesis of an extensive series of ultrafine and homogeneous alloy MMNCs, achieved by 1) a flexible compositional design by formulation in the precursor solution phase and 2) the ultrafast synthesis of alloy MMNCs using thermal shock heating (i.e., ∼1,650 K, ∼500 ms). This approach is remarkably facile and easily accessible compared to conventional vapor-phase deposition, and the particle size and structural uniformity enable comparative studies across compositionally different MMNCs. Rapid electrochemical screening is demonstrated by using a scanning droplet cell, enabling us to discover two promising electrocatalysts, which we subsequently validated using a rotating disk setup. This demonstrated high-throughput material discovery pipeline presents a paradigm for facile and accelerated exploration of MMNCs for a broad range of applications.more » « less
-
more » « less
Abstract Metal cation‐doped lead halide perovskite (LHP) quantum dots (QDs) with photoluminescence quantum yields (PLQYs) higher than unity, due to quantum cutting phenomena, are an important building block of the next‐generation renewable energy technologies. However, synthetic route exploration and development of the highest‐performing QDs for device applications remain challenging. In this work, Smart Dope is presented, which is a self‐driving fluidic lab (SDFL), for the accelerated synthesis space exploration and autonomous optimization of LHP QDs. Specifically, the multi‐cation doping of CsPbCl3QDs using a one‐pot high‐temperature synthesis chemistry is reported. Smart Dope continuously synthesizes multi‐cation‐doped CsPbCl3QDs using a high‐pressure gas‐liquid segmented flow format to enable continuous experimentation with minimal experimental noise at reaction temperatures up to 255°C. Smart Dope offers multiple functionalities, including accelerated mechanistic studies through digital twin QD synthesis modeling, closed‐loop autonomous optimization for accelerated QD synthetic route discovery, and on‐demand continuous manufacturing of high‐performing QDs. Through these developments, Smart Dope autonomously identifies the optimal synthetic route of Mn‐Yb co‐doped CsPbCl3QDs with a PLQY of 158%, which is the highest reported value for this class of QDs to date. Smart Dope illustrates the power of SDFLs in accelerating the discovery and development of emerging advanced energy materials.
-
Bimetallic nanoparticles often show properties superior to their single-component counterparts. However, the large parameter space, including size, structure, composition, and spatial arrangement, impedes the discovery of the best nanoparticles for a given application. High-throughput methods that can control the composition and spatial arrangement of the nanoparticles are desirable for accelerated materials discovery. Herein, we report a methodology for synthesizing bimetallic alloy nanoparticle arrays with precise control over their composition and spatial arrangement. A dual-channel nanopipet is used, and nanofluidic control in the nanopipet further enables precise tuning of the electrodeposition rate of each element, which determines the final composition of the nanoparticle. The composition control is validated by finite element simulation as well as electrochemical and elemental analyses. The scope of the particles demonstrated includes Cu–Ag, Cu–Pt, Au–Pt, Cu–Pb, and Co–Ni. We further demonstrate surface patterning using Cu–Ag alloys with precise control of the location and composition of each pixel. Additionally, combining the nanoparticle alloy synthesis method with scanning electrochemical cell microscopy (SECCM) allows for fast screening of electrocatalysts. The method is generally applicable for synthesizing metal nanoparticles that can be electrodeposited, which is important toward developing automated synthesis and screening systems for accelerated material discovery in electrocatalysis.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d1nr05497j
