Cadmium-Free and Efficient Type-II InP/ZnO/ZnS Quantum Dots and Their Application for LEDs

Eren, Guncem Ozgun; Sadeghi, Sadra; Jalali, Houman Bahmani; Ritter, Maximilian; Han, Mertcan; Baylam, Işınsu; Melikov, Rustamzhon; Önal, Asım; Öz, Fatma Nur; Şahin, Mehmet; Ow‐Yang, Cleva W.; Sennaroğlu, Alphan; Lechner, R. T.; Nizamoğlu, Sedat

doi:10.1021/acsami.1c08118

Cited by 53 publications

(45 citation statements)

References 68 publications

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“…Indium phosphide quantum dots (QDs) are a promising alternative to traditional Cd-and Pb-based materials for lighting, displays, and optoelectronic technologies [1][2][3] . However, due to its increased covalency, limitations in easily accessible precursors, and inherent distinctions in precursor reactivity and valency, the synthesis of InP has been met by more challenges when compared to their II-VI counterparts in terms of extracting generalizable design principles and targeted properties 4 .…”

Section: Introductionmentioning

confidence: 99%

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

et al. 2022

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The prediction of chemical reaction outcomes using machine learning (ML) has emerged as a powerful tool for advancing materials synthesis. However, this approach requires large and diverse datasets which are extremely limited in the field of nanomaterials synthesis, due to inconsistent and nonstandardized reporting in the literature, and a lack of understanding of synthetic mechanisms. In this study, we extracted parameters of InP quantum dot (QD) syntheses as our inputs, and resultant properties (absorption, emission, diameter) as our outputs from 72 publications. We "filled in" missing outputs using a data imputation method to prepare a complete dataset containing 216 entries for training and testing predictive ML models. We defined the descriptor space in two ways (condensed and extended) based on the chemical identity or role of reagents to explore the best approach for categorizing input features. We achieved mean absolute errors (MAEs) as low as 20.29, 11.46, and 0.33 nm for absorption, emission, and diameter respectively with our best ML model. We used these models to deploy an accessible and interactive webapp for designing syntheses of InP (https://share.streamlit.io/cossairt-lab/indiumphosphide/Hot_injection/hot_injection_prediction.py). Using this webapp, we investigated the power of ML to uncover chemical trends in InP syntheses, such as the effects of common additives. We also designed and conducted new experiments based on extensions of literature procedures and compared our experimentally measured properties to predictions, thus evaluating the "real-life" accuracy of our models. Conversely, we designed an experiment to obtain InP QDs with specific properties. Finally, we applied the same approach to train, test, and launch predictive models for CdSe QDs by expanding a previously published dataset. Altogether, our data pre-processing method and ML implementations in this study show the ability to design materials with targeted properties and explore underlying reaction mechanisms despite limited data resources.

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Section: Introductionmentioning

confidence: 99%

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

et al. 2022

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“…al. 48 and Baskoutas & Terzis 49 , but the curve after imputing deviates in the larger size range The inverse square fitted sizing curve for the data before imputing is (1) and the curve for the data after imputing is (2) where E0 is the band gap in eV and d is the QD diameter in nm. The most likely reason for the deviation in the curves at larger sizes is the limited available data for high quality InP QDs larger than 4 nm.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Indium phosphide quantum dots (QDs) are a promising alternative to traditional Cd-and Pb-based materials for lighting, displays, and optoelectronic technologies [1][2][3] . However, due to its increased covalency, limitations in easily accessible precursors, and inherent distinctions in precursor reactivity and valency, the synthesis of InP has been met by more challenges when compared to their II-VI and IV-VI counterparts in terms of extracting generalizable design principles and targeted properties 4 .…”

Section: Introductionmentioning

confidence: 99%

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Nguyen

Dou

Park

et al. 2022

Preprint

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Predictions of chemical reaction outcomes using machine learning (ML) has emerged as a powerful tool for advancing materials synthesis. However, this approach requires large and diverse datasets, which are extremely limited in the field of nanomaterials synthesis due to inconsistent and non-standardized reporting in the literature and a lack of understanding of synthetic mechanisms. In this study, we extracted parameters of InP quantum dot (QD) syntheses as our inputs, and resultant properties (absorption, emission, diameter) as our outputs from 72 publications. We “filled in” missing outputs using a data imputation method to prepare a complete dataset containing 216 entries for training and testing predictive ML models. We defined the descriptor space in two ways (condensed and extended) based on either chemical identity or the role of reagents to explore the best approach for categorizing input features. We achieved mean absolute errors (MAEs) as low as 20.29, 11.46, and 0.33 nm for absorption, emission, and diameter respectively with our best ML model across diverse synthetic methods. We used these models to deploy an accessible and interactive webapp for designing syntheses of InP (https://share.streamlit.io/cossairt-lab/indium-phosphide/Hot_injection/hot_injection_prediction.py). Using this webapp, we investigated the power of ML to uncover chemical trends in InP syntheses, such as the effects of common additives, like zinc salts and trioctylphosphine. We also designed and conducted new experiments based on extensions of literature procedures and compared our experimentally measured properties to predictions, thus evaluating the “real-life” accuracy of our models. Conversely, we used inverse-design to obtain InP QDs with specific properties. Finally, we applied the same approach to train, test, and launch predictive models for CdSe QDs by expanding a previously published dataset. Altogether, our data pre-processing method and ML implementations demonstrate the ability to design materials with targeted properties and explore underlying reaction mechanisms even when faced with limited data resources.

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“…Alternatively, liquid-state integration allows for sensitive tuning of optical device characteristics by simple injection of QDs, enables suppression of the host-material effect, and keeps a high photoluminescence quantum yield (PLQY) of the QDs in the device architectures. − In addition to its benefits for efficiency, liquid-state integration can be advantageous in terms of thermal management of LEDs as well. , The investigation of the remote and liquid-state QD-LEDs showed that liquid can keep the temperature of the device as low as 50 °C, while in remote QD-LEDs, the temperature can elevate to 200 °C, especially at high injection currents . Thus, the usage of QDs in a liquid form in the device architecture keeps the surface temperature low and enhances the device’s reliability .…”

Section: Introductionmentioning

confidence: 99%

High-Performance White Light-Emitting Diodes over 150 lm/W Using Near-Unity-Emitting Quantum Dots in a Liquid Matrix

et al. 2022

Self Cite

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In the next decade, we will witness the replacement of a majority of conventional light sources with light-emitting diodes (LEDs). Efficient LEDs other than phosphors can enhance their functionality and meet different lighting needs. Quantum dots (QDs) have high potential for future LED technology due to their sensitive band-gap tuning via the quantum confinement effect and compositional control, high photoluminescence quantum yield (PLQY), and mass-production capacity. Herein, we demonstrate white LEDs using QDs that reach over 150 lumens per electrical Watt. For that we synthesized green- and red-emitting ZnCdSe/ZnSe core/shell QDs by low-temperature nucleation, high-temperature shell formation, and postsynthetic trap-state removal. Their cadmium concentration is lower than 100 ppm, satisfying the current EU RoHS regulations, and their PLQY reaches a high level of 94%. The PLQY of QDs is maintained within the device on blue LED via liquid injection, and their integration at optimized optical densities leads to 129.6 and 170.4 lm/W for red-green-blue (RGB)- and green-blue (GB)-based white LEDs, respectively. Our simulations further showed that an efficiency level of over 230 lm/W is achievable using ultraefficient blue LED pumps. The simple fabrication and high performance of white LEDs using QD liquids show high promise for next-generation lighting devices.

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Cadmium-Free and Efficient Type-II InP/ZnO/ZnS Quantum Dots and Their Application for LEDs

Cited by 53 publications

References 68 publications

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

Predicting Indium Phosphide Quantum Dot Properties from Synthetic Procedures Using Machine Learning

High-Performance White Light-Emitting Diodes over 150 lm/W Using Near-Unity-Emitting Quantum Dots in a Liquid Matrix

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