Searching across hundreds of databases
This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.
Efficient low-grade heat recovery can help to reduce greenhouse gas emission as over 70% of primary energy input is wasted as heat, but current technologies to fulfill the heat-to-electricity conversion are still far from optimum. Here we report a direct thermal charging cell, using asymmetric electrodes of a graphene oxide/platinum nanoparticles cathode and a polyaniline anode in Fe2+/Fe3+ redox electrolyte via isothermal heating operation. When heated, the cell generates voltage via a temperature-induced pseudocapacitive effect of graphene oxide and a thermogalvanic effect of Fe2+/Fe3+, and then discharges continuously by oxidizing polyaniline and reducing Fe3+ under isothermal heating till Fe3+ depletion. The cell can be self-regenerated when cooled down. Direct thermal charging cells attain a temperature coefficient of 5.0 mV K-1 and heat-to-electricity conversion efficiency of 2.8% at 70 °C (21.4% of Carnot efficiency) and 3.52% at 90 °C (19.7% of Carnot efficiency), outperforming other thermoelectrochemical and thermoelectric systems.
Explaining colour variation among animals at broad geographic scales remains challenging. Here we demonstrate how deep learning-a form of artificial intelligence-can reveal subtle but robust patterns of colour feature variation along an ecological gradient, as well as help identify the underlying mechanisms generating this biogeographic pattern. Using over 20,000 images with precise GPS locality information belonging to nearly 2,000 moth species from Taiwan, our deep learning model generates a 2048-dimension feature vector that accurately predicts each species' mean elevation based on colour and shape features. Using this multidimensional feature vector, we find that within-assemblage image feature variation is smaller in high elevation assemblages. Structural equation modeling suggests that this reduced image feature diversity is likely the result of colder environments selecting for darker colouration, which limits the colour diversity of assemblages at high elevations. Ultimately, with the help of deep learning, we will be able to explore the endless forms of natural morphological variation at unpreceded depths.
Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the facets that you can filter your papers by.
From here we'll present any options for the literature, such as exporting your current results.
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Year:
Count:
The SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
