Bioluminescence

Lee, John

doi:10.1111/j.1751-1097.1974.tb06617.x

Cited by 5 publications

(2 citation statements)

References 79 publications

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“…Due to the pH buffering effects of the RPMI 1640 solution, the change in the pH is negligible. Although pH alone cannot be used to measure the total ion concentration (pH indicates only H ions), it should be related to the role of the electrode on the liquid surface, because it is related to the ionization rate, e. g., HONO, which is a conjugate of NO 2 − (HONO↔NO 2 − +H + ), displays a pKa of 3.3–3.8 [52–54] . A higher pH indicates a higher ionization rate for the formation of NO 2 − and H + .…”

Section: Resultsmentioning

confidence: 99%

Characteristics of Aqueous Chemical Species Generation in Plasma‐Facing Liquid Systems Using Helium Jet Plasma

Park,

Bae,

Lee

2024

ChemistryOpen

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Plasma‐facing liquids (PFLs) facilitate the storage of reactive O and N species (RONS), including H2O2 and NO2−, which remain in the PFL after plasma treatment, and they can continuously influence the target immersed in the liquid. However, their behaviors and levels of generation and extinction depend strongly on the plasma characteristics and liquid condition. Therefore, understanding the effects of the liquid type on the plasma discharge characteristics and the RONS generated via plasma discharge is necessary. We compared the RONS generation and storage trends of deionized H2O and a high‐conductivity PFL, RPMI 1640, which is a well‐known cell culture medium commonly used to culture mammalian cells. RPMI 1640 acted as an electrode and enhanced the plasma discharge power by supplying abundant radicals and RONS. The production of gaseous hydroxyl radicals and NO markedly increased, which facilitated H2O2 and NO2− production in the PFL for the first 200 s, and then the increase in the RONS concentration stagnated. With respect to storage, as the components within RMPI 1640 exhibited high reaction constants for their reactions with H2O2, H2O2 elimination was completed in <30 min. Unlike H2O2, the concentration of NO2− in the PFL was unchanged.

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Section: Resultsmentioning

confidence: 99%

Characteristics of Aqueous Chemical Species Generation in Plasma‐Facing Liquid Systems Using Helium Jet Plasma

Park,

Bae,

Lee

2024

ChemistryOpen

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“…Their model reported an R 2 of 0.75, a Root Mean Square Error (RMSE) of 1.39%, and a Mean Absolute Error (MAE) of 1.04% on the test dataset. In foundational work by Lee, the utilization of the XGBoost algorithm was explored, leveraging experimentally derived properties to unravel the complex interplay between the electronic characteristics of materials and crucial photovoltaic parameters such as PCE, [ 14 ] open‐circuit voltage (V O c), [ 15 ] short circuit current (Jsc), [ 16 ] and fill factor (FF) [ 17 ] in non‐fullerene ternary OSCs. Importantly, Lee highlighted the significance of the electronegativity of the primary donor within these systems, [ 14 ] a parameter inferred from the experimentally determined HOMO and LUMO energy levels, as a critical factor in the advancement of efficient non‐fullerene ternary OSCs.…”

Section: Introductionmentioning

confidence: 99%

Extrapolative Machine Learning for Accurate Efficiency Prediction in Non‐Fullerene Ternary Organic Solar Cells: Leveraging Computable Molecular Descriptors in High‐Throughput Virtual Screening

Liao,

Tsai

2024

Solar RRL

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Adding a third component to binary organic solar cells (OSCs) enhances ternary OSCs, boosting power conversion efficiency (PCE). However, developing and optimizing appropriate donors, acceptors, and ternary materials remains a complex and demanding task. This study presents four machine‐learning (ML) predictive models using XGBoost and ANN approaches, utilizing both experimental and DFT‐derived HOMO and LUMO levels for efficient high‐throughput virtual screening (HTVS) of top candidates based on PCE. Two distinct latent databases were employed for HTVS: one consisting of 429,413 uniquely recombined ternary OSC systems from experimentally available data, and another comprising approximately 2.3 million unique donor molecules from the Harvard Clean Energy Project database (CEPDB). The four ML models demonstrated notable predictive accuracy for PCE on a test dataset containing molecules closely aligned with the training set (interpolation). However, the XGBoost model showed constrained extrapolative ability for molecules significantly divergent from those in the training dataset. In contrast, the ANN models displayed a robust extrapolative capacity in HTVS, successfully predicting new potential ternary OSC systems and leading donors with PCE values exceeding 20%. Our ML models use HOMO and LUMO inputs for donors, acceptors, and ternaries, facilitating efficient optimization via rapid HTVS of high‐performance ternary materials.This article is protected by copyright. All rights reserved.

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Energy Transfer Processes in Bioluminescence

Ward¹

1979

Photochemical and Photobiological Reviews

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Bioluminescence

Cited by 5 publications

References 79 publications

Characteristics of Aqueous Chemical Species Generation in Plasma‐Facing Liquid Systems Using Helium Jet Plasma

Characteristics of Aqueous Chemical Species Generation in Plasma‐Facing Liquid Systems Using Helium Jet Plasma

Extrapolative Machine Learning for Accurate Efficiency Prediction in Non‐Fullerene Ternary Organic Solar Cells: Leveraging Computable Molecular Descriptors in High‐Throughput Virtual Screening

Energy Transfer Processes in Bioluminescence

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