Physikalisch-Chemische Tabellen

Landolt, H.; Börnstein, Richard

doi:10.1007/978-3-662-24670-2_1

Cited by 28 publications

(10 citation statements)

References 133 publications

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“… 1, Aikazyan and Fedorova (1952) d ; 2, Baird and Davidson (1962) d ; 3, Boerboom and Kleyn (1969); 4, Brdicka and Wiesner (1947) d ; 5, Carlson (1911) d ; 6, Chiang and Toor (1959) d ; 7, Clarke (1964) d ; 8, Daubert and Danner (1994); 9, Davidson and Cullen (1957) d ; 10, Davies et al (1964) d ; 11, Davis et al (1967) d ; 12, Duda and Vrentas (1968) d ; 13, Eggleton et al (1945) d ; 14, Ferrell and Himmelblau (1967) d ; 15, Gertz and Loeschcke (1954) d ; 16, Gertz and Loeschcke (1956) d ; 17, Grote (1967) d ; 18, Grote and Thews (1962) d ; 19, Gubbins and Walker (1965) d ; 20, Hagenbach (1898) d ; 21, Landolt et al (1912); 22, Han and Bartels (1996); 23, Himmelblau (1964) d ; 24, Houghton et al (1962); 25, Hüfner (1897) d ; 26, Hüfner (1898) d ; 27, Ipatieff et al (1933) d ; 28, Ipatieff and Teodorovitsch (1937) d ; 29, Jähne et al (1987); 30, Jordan et al (1956) d ; 31, Kolthoff and Miller (1941) d ; 32, Kreuzer (1950) d ; 33, Krieger et al (1967) d ; 34, Lide (2010); 35, Müller (1948) d ; 36, Nijsing et al (1959) d ; 37, O'Brien and Hyslop (1977); 38, Rehm et al (1963) d ; 39, Reid et al (1987); 40, Ringbom (1938) d ; 41, Rüetschi (1967) d ; 42, Smith et al (1955) d ; 43, Stefan (1878) d ; 44, Tammann and Jessen (1929) d ; 45, Tang and Himmelblau (1963) d ; 46, Tham et al (1970) d ; 47, Thomas and Adams (1965) d ; 48, Unver and Himmelblau (1964) d ; 49, Viswanath and Natarajan (1989)…”

Section: Resultsmentioning

confidence: 99%

Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers

Raymond

Zappa

Butman

et al. 2012

Limn Fluids and Environments

549

819

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Lay Abstract The exchange of gasses between water and air is important to the budgets of carbon, nutrients, and pollutants. This exchange is driven, in part, by the turbulent energy at the air–water interface. Turbulent energy at the air–water interface scales with the gas transfer velocity (k), which can be measured in streams through various methods. We performed a metadata analysis of studies that have measured k in streams using direct gas tracer releases. We evaluated models that predict k based on stream morphology. We found that models that use slope and velocity to predict k perform reasonably well and are consistent with general theory. We also used the data set to provide new stream hydraulic equations that predict stream morphology (width, depth, velocity) based on discharge.

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Section: Resultsmentioning

confidence: 99%

Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers

Raymond

Zappa

Butman

et al. 2012

Limn Fluids and Environments

549

819

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“…A u t h o r ' s P o s t -p r i n t Table 1 Effective hyperfine coupling constants in mT for radical pairs formed by the electron donors and acceptors used in this study, calculated from literature values [16][17][18][19] using Eq. 13.…”

Section: Simulation Methodsmentioning

confidence: 99%

Spin-locking in low-frequency reaction yield detected magnetic resonance

Wedge

Lau

Ferguson

et al. 2013

Phys. Chem. Chem. Phys.

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The purported effects of weak magnetic fields on various biological systems from animal magnetoreception to human health have generated widespread interest and sparked much controversy in the past decade. To date the only well established mechanism by which the rates and yields of chemical reactions are known to be influenced by magnetic fields is the radical pair mechanism, based on the spin-dependent reactivity of radical pairs. A diagnostic test for the operation of the radical pair mechanism was proposed by Henbest et al. [J. Am. Chem. Soc., 2004, 126, 8102] based on the combined effects of weak static magnetic fields and radiofrequency oscillating fields in a reaction yield detected magnetic resonance experiment. Here we investigate the effects on radical pair reactions of applying relatively strong oscillating fields, both parallel and perpendicular to the static field. We demonstrate the importance of understanding the effect of the strength of the radiofrequency oscillating field; our experiments demonstrate that there is an optimal oscillating field strength above which the observed signal decreases in intensity and eventually inverts. We establish the correlation between the onset of this effect and the hyperfine structure of the radicals involved, and identify the existence of 'overtone' type features appearing at multiples of the expected resonance field position.

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“…Similarly, there are several well-established compilations of measured thermodynamic properties and general materials physical chemistry data. [43][44][45][46] However, many existing experimental data repositories are still either too small or too heterogeneous (e.g., different experimental conditions or measurement techniques) for high quality ML models. Human bias can also affect data diversity.…”

Section: Data Collectionmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

422

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Physikalisch-Chemische Tabellen

Cited by 28 publications

References 133 publications

Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers

Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers

Spin-locking in low-frequency reaction yield detected magnetic resonance

A Critical Review of Machine Learning of Energy Materials

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