The W2024 database of the water isotopologue H 2 16 O .

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-28 DOI:10.1038/s41597-024-03847-3
Tibor Furtenbacher, Roland Tóbiás, Jonathan Tennyson, Robert R Gamache, Attila G Császár
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Abstract

The rovibrational spectrum of the water molecule is the crown jewel of high-resolution molecular spectroscopy. While its significance in numerous scientific and engineering applications and the challenges behind its interpretation have been well known, the extensive experimental analysis performed for this molecule, from the microwave to the ultraviolet, is admirable. To determine empirical energy levels for H 2 16 O , this study utilizes an improved version of the MARVEL (Measured Active Rotational-Vibrational Energy Levels) scheme, which now takes into account multiplet constraints and first-principles energy-level splittings. This analysis delivers 19027 empirical energy values, with individual uncertainties and confidence intervals, utilizing 309 290 transition wavenumbers collected from 189 (mostly experimental) data sources. Relying on these empirical, as well as some computed, energies and first-principles intensities, an extensive composite line list, named CW2024, has been assembled. The CW2024 dataset is compared to lines in the canonical HITRAN 2020 spectroscopic database, providing guidance for future experimental investigations.

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W2024 水同位素 H 2 16 O 数据库。
水分子的振动光谱是高分辨率分子光谱学的皇冠上的明珠。虽然它在众多科学和工程应用中的重要性及其解释背后的挑战已众所周知,但对该分子进行的从微波到紫外线的广泛实验分析令人钦佩。为了确定 H 2 16 O 的经验能级,本研究采用了 MARVEL(测量到的有源旋转振动能级)方案的改进版本,该方案现在考虑到了多重约束和第一原理能级分裂。这项分析利用从 189 个数据源(主要是实验数据源)收集到的 309 290 个转变波文数,提供了 19027 个经验能量值,其中包括各个不确定性和置信区间。根据这些经验值以及一些计算值、能量和第一原理强度,我们编制了一份内容广泛的复合线表,命名为 CW2024。CW2024 数据集与 HITRAN 2020 光谱数据库中的典型谱线进行了比较,为未来的实验研究提供了指导。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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