The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Understanding Chemical versus Electrostatic Shifts in X‑ray Photoelectron Spectra of Organic Self-Assembled Monolayers

The focus of the present article is on understanding the insight that X-ray photoelectron spectroscopy (XPS) measurements can provide when studying self-assembled monolayers. Comparing density functional theory calculations to experimental data on deliberately chosen model systems, we show that both the chemical environment and electrostatic effects arising from a superposition of molecular dipoles influence the measured core-level binding energies to a significant degree. The crucial role of the often overlooked electrostatic effects in polar self-assembled monolayers (SAMs) is unambiguously demonstrated by changing the dipole density through varying the SAM coverage. As a consequence of this effect, care has to be taken when extracting chemical information from the XP spectra of ordered organic adsorbate layers. Our results, furthermore, imply that XPS is a powerful tool for probing local variations in the electrostatic energy in nanoscopic systems, especially in SAMs

Effects of Embedded Dipole Layers on Electrostatic Properties of Alkanethiolate Self-Assembled Monolayers

Alkanethiolates (ATs) forming self-assembled monolayers (SAMs) on coinage metal and semiconductor substrates have been used successfully for decades for tailoring the properties of these surfaces. Here, we provide a detailed analysis of a highly promising class of AT-based systems, which are modified by one or more dipolar carboxylic acid ester groups embedded into the alkyl backbone. To obtain comprehensive insight, we study nine different embedded-dipole monolayers and five reference nonsubstituted SAMs. We systematically varied lengths of the alkyl segments, ester group orientations, and number of ester groups contained in the chain. To understand the structural and electronic properties of the SAMs, we employ a variety of complementary experimental techniques, namely, infrared reflection absorption spectroscopy (IRS), high-resolution X-ray photoelectron spectroscopy (XPS), ultraviolet photoelectron spectroscopy (UPS), atomic force microscopy (AFM), and Kelvin probe (KP) AFM. These experiments are complemented with state-of-the-art electronic band-structure calculations. We find intriguing electronic properties such as large and variable SAM-induced work function modifications and dipole-induced shifts of the electrostatic potential within the layers. These observations are analyzed in detail by joining the results of the different experimental techniques with the atomistic insight provided by the quantum-mechanical simulations

Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing

Abstract Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics