Anisotropy and spatial restriction of conduction electron diffusion in perylene radical cation salt

Dormann, E.; Wokrina, Tim

doi:10.1016/s0379-6779(97)81087-9

Cited by 3 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the cases of perylene or fluoroanthene salts, Peierls phase transitions have been observed upon cooling, inducing a large modification of the spin relaxation. Moreover, because of the extremely narrow lines in the metallic phase, pulsed EPR was successfully applied to deduce the longitudinal and transverse relaxation times. − In particular, a decay of the correlation function as exp(−( t /τ) 3/2 ), characteristic of one-dimensional systems was observed . Accurate low field magnetometers were built with these materials taking advantage of the very small line width. , Unfortunately, this application has been limited by the instability of these materials at room temperature.…”

Section: 6 Other Selected Epr Resultsmentioning

confidence: 99%

“…Moreover, because of the extremely narrow lines in the metallic phase, pulsed EPR was successfully applied to deduce the longitudinal and transverse relaxation times. [290][291][292][293][294][295][296][297][298][299][300] In particular, a decay of the correlation function as exp(-(t/τ) 3/2 ), characteristic of one-dimensional systems was observed. 301 Accurate low field magnetometers were built with these materials taking advantage of the very small line width.…”

Section: Other Selected Epr Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Electron Spin Resonance: A Major Probe for Molecular Conductors

Coulon

Clérac

2004

Chem. Rev.

View full text Add to dashboard Cite

Section: 6 Other Selected Epr Resultsmentioning

confidence: 99%

Section: Other Selected Epr Resultsmentioning

confidence: 99%

Electron Spin Resonance: A Major Probe for Molecular Conductors

Coulon

Clérac

2004

Chem. Rev.

View full text Add to dashboard Cite

“…The mean of the plots included in the grid is calculated and used as a covariate to account for the spatial variation [28]. In contrast with the spatial autoregressive (SAR) model, which treats spatial variation as anisotropic along different directions [29], the moving mean average models treat the spatial variation as isotropic, in which the covariates among neighbor plots are simple means of the neighbor plots within a user-defined grid. This approach has been reported to adjust the spatial variation successfully and thus has increased genomic selection accuracy from 0.231 to 0.37 for grain yield and from 0.436 to 0.614 for days to heading in wheat breeding, respectively [30].…”

Section: Introductionmentioning

confidence: 99%

Applying Spatial Statistical Analysis to Ordinal Data for Soybean Iron Deficiency Chlorosis

Cannon

Beavis

2022

Agronomy

View full text Add to dashboard Cite

Accounting for field variation patterns plays a crucial role in interpreting phenotype data and, thus, in plant breeding. Several spatial models have been developed to account for field variation. Spatial analyses show that spatial models can successfully increase the quality of phenotype measurements and subsequent selection accuracy for continuous data types such as grain yield and plant height. The phenotypic data for stress traits are usually recorded in ordinal data scores but are traditionally treated as numerical values with normal distribution, such as iron deficiency chlorosis (IDC). The effectiveness of spatial adjustment for ordinal data has not been systematically compared. The research objective described here is to evaluate methods for spatial adjustment of ordinal data, using soybean IDC as an example. Comparisons of adjustment effectiveness for spatial autocorrelation were conducted among eight different models. The models were divided into three groups: Group I, moving average grid adjustment; group II, geospatial autoregressive regression (SAR) models; and Group III, tensor product penalized P-splines. Results from the model comparison show that the effectiveness of the models depends on the severity of field variation, the irregularity of the variation pattern, and the model used. The geospatial SAR models outperform the other models for ordinal IDC data. Prediction accuracy for the lines planted in the IDC high-pressure area is 11.9% higher than those planted in low-IDC-pressure regions. The relative efficiency of the mixed SAR model is 175%, relative to the baseline ordinary least squares model. Even though the geospatial SAR model is the best among all the compared models, the efficiency is not as good for ordinal data types as for numeric data.

show abstract

“…The mean of the plots included in the grid is calculated and used as a covariate to account for the spatial variation [30]. In contrast with the spatial autoregressive (SAR) model, which treats spatial variation anisotropic along different directions [31], the moving mean average models treat spatial variation as isotropic, in which the covariates among neighbor plots are simple means of the neighbor plots within a user-defined grid. This approach has been reported to adjust the spatial variation successfully and thus has increased genomic selection accuracy from 0.231 to 0.37 for grain yield and from 0.436 to 0.614 for days to heading for wheat breeding projects [32].…”

Section: Introductionmentioning

confidence: 99%

Applications of spatial models to ordinal data

Cannon

Beavis

2020

Preprint

View full text Add to dashboard Cite

Models have been developed to account for heterogeneous spatial variation in field trials. These spatial models have been shown to successfully increase the quality of phenotypic data resulting in improved effectiveness of selection by plant breeders. The models were developed for continuous data types such as grain yield and plant height, but data for most traits, such as in iron deficiency chlorosis (IDC), are recorded on ordinal scales. Is it reasonable to make spatial adjustments to ordinal data by simply applying methods developed for continuous data? The objective of the research described herein is to evaluate methods for spatial adjustment on ordinal data, using soybean IDC as an example. Spatial adjustment models are classified into three different groups: group I, moving average grid adjustment; group II, geospatial autoregressive regression (SAR) models; and group III, tensor product penalized P-splines. Comparisons of eight models sampled from these three classes demonstrate that spatial adjustments depend on severity of field heterogeneity, the irregularity of the spatial patterns, and the model used. SAR models generally produce better performance metrics than other classes of models. However, none of the eight evaluated models fully removed spatial patterns indicating that there is a need to either adjust existing models or develop novel models for spatial adjustments of ordinal data collected in fields exhibiting discontinuous transitions between heterogeneous patches.

show abstract

Anisotropy and spatial restriction of conduction electron diffusion in perylene radical cation salt

Cited by 3 publications

References 8 publications

Electron Spin Resonance: A Major Probe for Molecular Conductors

Electron Spin Resonance: A Major Probe for Molecular Conductors

Applying Spatial Statistical Analysis to Ordinal Data for Soybean Iron Deficiency Chlorosis

Applications of spatial models to ordinal data

Contact Info

Product

Resources

About