The summary in the paper is perhaps clearer than the article about it:
> "Our study has significant implications for the fundamental understanding of defect tolerance in these materials and the design of halide perovskite solar cells, in particular for tandem cells. Using a novel suite of multimodal microscopy techniques, we unveil the remarkably complex energetic landscape that charge carriers must navigate in halide perovskites. We provide the first nanoscale picture of how this energetic landscape influences photodoping, carrier recombination and trapping."
> "We find that the pursuit of homogeneous chemical compositions is not necessarily the best way to maximize the performance of this family of semiconductors, at least while the material still possesses deep trap clusters that lower device performance from the radiative limits. The existence of mixed Br and I samples induces the formation of beneficial local heterostructures that confer enhanced defect tolerance to these materials. In these regions, charge-carrier photogeneration and radiative recombination occurs through a rapid wide-to-narrow bandgap funneling process, more efficient than in the chemically homogeneous counterparts."
What's really interesting is how these materials are heterogenous at the nanoscale, which is rather like how the biological light-harvesting protein complexes(LHC) operate, with ordered aligned arrays of chlorophyll molecules held in particular orientations within the protein structure that optimize funneling of photon energy into reaction centers (water-splitting for H2 production).
However, these materials may not ever be commercially successful, due to issues like lead pollution and the working lifetime of the materials (they degrade fairly rapidly in full sun). Regardless, this is still very useful and important basic research.
However you could, in an industrial setting, do something like this:
"Integration of a Hydrogenase in a Lead Halide Perovskite Photoelectrode for Tandem Solar Water Splitting 2020"
Here you could use a catalyst regeneration strategy where you basically have a little production line onsite and as each unit wears out you just pop in a new one and send the old one to be regenerated, that's more plausible.
There lifetime is too long to have enough locally and if we have 100% renewable, transport will be no ecological issue anymore.
This statement fragment makes no sense. Silicon is the second most abundant element in the Earth's crust, after oxygen. If it means the specific form in crystalline silicon, well that doesn't occur in nature, but then neither do these lead salts.
It's not even relevant to call it "microscopy" anymore, we require a new term. It's a complete thin film atlas of all interacting forces of nature. Better data for the models, means higher fidelity simulations.
The question is can AI predict new materials? Can a simulation be sophisticated enough to predict say, high temperature superconductivity in rare earth cuprate perovskites?
Because how important is for human life, a compiler industry that finds ways to translate complicated simulations to AI algorithms could be the next big thing.
Lots of other options for viability im sure.
Better late then never I guess.