Abstract Electronic and excitonic states in an InSb strongly flattened ellipsoidal quantum dot (QD) with complicated dispersion law are theoretically investigated within the framework of the geometric adiabatic approximation in the strong, intermediate, and weak quantum confinement regimes. For the lower levels of the spectrum, the square root dependence of energy on QD sizes is revealed in the case of Kane’s dispersion law. The obtained results are compared to the case of a parabolic (standard) dispersion law of charge carriers. The possibility of the accidental exciton instability is revealed for the intermediate quantum confinement regime. For the weak quantum confinement regime, the motion of the exciton's center-of-gravity is quantized, which leads to the appearance of additional Coulomb-like sub-levels. It is revealed that in the case of the Kane dispersion law, the Coulomb levels shift into the depth of the forbidden band gap, moving away from the quantum confined level, whereas in the case of the parabolic dispersion law, the opposite picture is observed. The corresponding selection rules of quantum transitions for the interband absorption of light are obtained. New selection rules of quantum transitions between levels conditioned by 2D exciton center of mass vertical motion quantization in a QD aremore »
Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot
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.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Advanced Materials
- Page Range or eLocation-ID:
- Sponsoring Org:
- National Science Foundation
More Like this
Comprehensive Electrostatic Modeling of Exposed Quantum Dots in Graphene/Hexagonal Boron Nitride HeterostructuresRecent experimental advancements have enabled the creation of tunable localized electrostatic potentials in graphene/hexagonal boron nitride (hBN) heterostructures without concealing the graphene surface. These potentials corral graphene electrons yielding systems akin to electrostatically defined quantum dots (QDs). The spectroscopic characterization of these exposed QDs with the scanning tunneling microscope (STM) revealed intriguing resonances that are consistent with a tunneling probability of 100% across the QD walls. This effect, known as Klein tunneling, is emblematic of relativistic particles, underscoring the uniqueness of these graphene QDs. Despite the advancements with electrostatically defined graphene QDs, a complete understanding of their spectroscopic features still remains elusive. In this study, we address this lapse in knowledge by comprehensively considering the electrostatic environment of exposed graphene QDs. We then implement these considerations into tight binding calculations to enable simulations of the graphene QD local density of states. We find that the inclusion of the STM tip’s electrostatics in conjunction with that of the underlying hBN charges reproduces all of the experimentally resolved spectroscopic features. Our work provides an effective approach for modeling the electrostatics of exposed graphene QDs. The methods discussed here can be applied to other electrostatically defined QD systems that are also exposed.
Release, detection and toxicity of fragments generated during artificial accelerated weathering of CdSe/ZnS and CdSe quantum dot polymer compositesNext generation displays and lighting applications are increasingly using inorganic quantum dots (QDs) embedded in polymer matrices to impart bright and tunable emission properties. The toxicity of some heavy metals present in commercial QDs ( e.g. cadmium) has, however, raised concerns about the potential for QDs embedded in polymer matrices to be released during the manufacture, use, and end-of-life phases of the material. One important potential release scenario that polymer composites can experience in the environment is photochemically induced matrix degradation. This process is not well understood at the molecular level. To study this process, the effect of an artificially accelerated weathering process on QD–polymer nanocomposites has been explored by subjecting CdSe and CdSe/ZnS QDs embedded in poly(methyl methacrylate) (PMMA) to UVC irradiation in aqueous media. Significant matrix degradation of QD–PMMA was observed along with measurable mass loss, yellowing of the nanocomposites, and a loss of QD fluorescence. While ICP-MS identified the release of ions, confocal laser scanning microscopy and dark-field hyperspectral imaging were shown to be effective analytical techniques for revealing that QD-containing polymer fragments were also released into aqueous media due to matrix degradation. Viability experiments, which were conducted with Shewanella oneidensis MR-1, showed a statistically significant decreasemore »
null (Ed.)Autonomous robotic experimentation strategies are rapidly rising in use because, without the need for user intervention, they can efficiently and precisely converge onto optimal intrinsic and extrinsic synthesis conditions for a wide range of emerging materials. However, as the material syntheses become more complex, the meta-decisions of artificial intelligence (AI)-guided decision-making algorithms used in autonomous platforms become more important. In this work, a surrogate model is developed using data from over 1000 in-house conducted syntheses of metal halide perovskite quantum dots in a self-driven modular microfluidic material synthesizer. The model is designed to represent the global failure rate, unfeasible regions of the synthesis space, synthesis ground truth, and sampling noise of a real robotic material synthesis system with multiple output parameters (peak emission, emission linewidth, and quantum yield). With this model, over 150 AI-guided decision-making strategies within a single-period horizon reinforcement learning framework are automatically explored across more than 600 000 simulated experiments – the equivalent of 7.5 years of continuous robotic operation and 400 L of reagents – to identify the most effective methods for accelerated materials development with multiple objectives. Specifically, the structure and meta-decisions of an ensemble neural network-based material development strategy are investigated, which offers a favorablemore »
The effect of static silica particles on the dynamics of quantum dot (QD) nanoparticles grafted with a poly(ethylene glycol) (PEG) brush in hydrogel nanocomposites is investigated using single particle tracking (SPT). At a low volume fraction of homogeneously dispersed silica ( Φ = 0.005), two distinct populations of PEG-QDs are observed, localized and mobile, whereas almost all PEG-QDs are mobile in neat hydrogel ( Φ = 0.0). Increasing the silica particle concentration ( Φ = 0.01, 0.1) results in an apparent change in the network structure, confounding the impact of silica on PEG-QD dynamics. The localized behavior of PEG-QDs is attributed to pH-mediated attraction between the PEG brush on the probe and surface silanol groups of silica. Using quartz crystal microbalance with dissipation (QCM-D), the extent of this interaction is investigated as a function of pH. At pH 5.8, the PEG brush on the probe can hydrogen bond with the silanol groups on silica, leading to adsorption of PEG-QDs. In contrast, at pH 9.2, silanol groups are deprotonated and PEG-QD is unable to hydrogen bond with silica leading to negligible adsorption. To test the effect of pH, PEG-QD dynamics are further investigated in hydrogel nanocomposites at Φ = 0.005. SPTmore »