Experimental Investigation on Capillary Water Absorption in Discrete Planar Cracks

Water movement is responsible for the deterioration of concrete and concrete structures, especially when distributed microcracks exist because cracks can facilitate the ingress of aggressive agents. Experiment was carried out on capillary water absorption by discrete planar cracks to clarify the effect of crack width on the transport speed of water by crack. The granite samples were used to create parallel and smooth cracks with purpose to avoid rehydration of the cement-based materials. Two granite blocks were applied to joint by glue for artificially fabricating a single parallel crack by means of ultra thin steel disc with various thicknesses of 50, 100, 150 and 200 mm. The capillary absorption test was conducted on the specimens according to the gravimetric method recommended in ASTM C1585. Mass of absorbed water by the single discrete crack was measured. It was found that the cumulative water mass of specimen generally increases with an increase of crack width for the ranges studied. The cumulative water mass rapidly increases for the initial stages of water absorption test while at later stages the rate of absorbed water is slowed down apparently

Universal method to extract the average electron spin relaxation in organic semiconductors from muonium ALC resonances

Muon spin spectroscopy and in particular the avoid level crossing (ALC) technique is a sensitive probe of electron spin relaxation (eSR) in organic semiconductors. In complex ALC spectra, eSR can be challenging to extract, as it requires the modelling of overlapping ALCs, where covariance between parameters can result in significant uncertainties. Here we demonstrate a general method to extract eSR rate, which is independent on the number of ALCs resonances present, whether they overlap or not, and what the muonium hyperfine (isotropic and anisotropic) parameters are. This can then be used to extract an accurate value for eSR rate and as guidance for undertaking experiments efficientl

Can aliphatic anchoring groups be utilised with dyes for p-type dye sensitized solar cells?

A series of novel laterally anchoring tetrahydroquinoline derivatives have been synthesized and investigated for their use in NiO-based p-type dye-sensitized solar cells. The kinetics of charge injection and recombination at the NiO-dye interface for these dyes have been thoroughly investigated using picosecond transient absorption and time-resolved infrared measurements. It was revealed that despite the anchoring unit being electronically decoupled from the dye structure, charge injection occurred on a sub picosecond timescale. However, rapid recombination was also observed due to the close proximity of the electron acceptor on the dyes to the NiO surface, ultimately limiting the performance of the p-DSCs

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.Peer reviewe