Artificial intelligence (AI) can help plant scientists collect and analyze unprecedented volumes of data, which would not be possible using conventional methods. Researchers at the University of Zurich (UZH) have now used big data, machine learning and field observations in the university’s experimental garden to show how plants respond to changes in the environment.

Climate change is making it increasingly important to know how plants can survive and thrive in a changing environment. Conventional experiments in the lab have shown that plants accumulate pigments in response to environmental factors. To date, such measurements were made by taking samples, which required a part of the plant to be removed and thus damaged. “This labor-intensive method isn’t viable when thousands or millions of samples are needed. Moreover, taking repeated samples damages the plants, which in turn affects observations of how plants respond to environmental factors. There hasn’t been a suitable method for the long-term observation of individual plants within an ecosystem,” says Reiko Akiyama, first author of the study.

With the support of UZH’s University Research Priority Program (URPP) “Evolution in Action”, a team of researchers has now developed a method that enables scientists to observe plants in nature with great precision. PlantServation is a method that incorporates robust image-acquisition hardware and deep learning-based software to analyze field images, and it works in any kind of weather.

Millions of images support evolutionary hypothesis of robustness

Using PlantServation, the researchers collected (top-view) images of Arabidopsis plants on the experimental plots of UZH’s Irchel Campus across three field seasons (lasting five months from fall to spring) and then analyzed the more than four million images using machine learning. The data recorded the species-specific accumulation of a plant pigment called “anthocyanin” as a response to seasonal and annual fluctuations in temperature, light intensity and precipitation.

PlantServation also enabled the scientists to experimentally replicate what happens after the natural speciation of a hybrid polyploid species. These species develop from a duplication of the entire genome of their ancestors, a common type of species diversification in plants. Many wild and cultivated plants such as wheat and coffee originated in this way.

In the current study, the anthocyanin content of the hybrid polyploid species A. kamchatica resembled that of its two ancestors: from fall to winter its anthocyanin content was similar to that of the ancestor species originating from a warm region, and from winter to spring it resembled the other species from a colder region. “The results of the study thus confirm that these hybrid polyploids combine the environmental responses of their progenitors, which supports a long-standing hypothesis about the evolution of polyploids,” says Rie Shimizu-Inatsugi, one of the study’s two corresponding authors.

From Irchel Campus to far-flung regions

PlantServation was developed in the experimental garden at UZH’s Irchel Campus. “It was crucial for us to be able to use the garden on Irchel Campus to develop PlantServation’s hardware and software, but its application goes even further: when combined with solar power, its hardware can be used even in remote sites. With its economical and robust hardware and open-source software, PlantServation paves the way for many more future biodiversity studies that use AI to investigate plants other than Arabidopsis – from crops such as wheat to wild plants that play a key role for the environment,” says Kentaro Shimizu, corresponding author and co-director of the URPP Evolution in Action.

The project is an interdisciplinary collaboration with LPIXEL, a company that specializes in AI image analysis, and Japanese research institutes at Kyoto University and the University of Tokyo, among others, under the Global Strategy and Partnerships Funding Scheme of UZH Global Affairs and the International Leading Research grant program of the Japan Society for the Promotion of Science (JSPS). The project also received funding from the Swiss National Science Foundation (SNSF).

Strategic Partnership with Kyoto University

Kyoto University is one of UZH’s strategic partner universities. The strategic partnership ensures that high-potential research collaborations will receive the necessary support to thrive, for instance through the UZH Global Strategy and Partnership Funding Scheme. Over the last years, several joint research projects between Kyoto University and UZH have already received funding, among them “PlantServation”.

Understanding how voltage drives nanoscale electrocatalysts to initiate reactions is a fundamental scientific question. This is especially challenging when dealing with non-metallic electrocatalysts due to their low inherent carrier concentration, which leads to poor conductivity. When voltage is applied at the non-metal/solution interface, the situation becomes more complex than in the case of metal/solution interfaces. One notable complexity is the significant potential drop within the non-metal, causing the surface potential to often deviate from the back potential. Analyzing the driving force for chemical reactions by applying classical metal models to non-metals can result in substantial inaccuracies. Up until now, distinguishing the potential distribution between the nonmetallic catalyst and the EDL still relies on complex theoretical calculations. The actual potential drop across the semiconductor-electrolyte interface remains unknown, due to the lacks of in in-situ techniques. Moreover, conventional electrochemical characterization only provides the ensemble information for electrode materials, neglecting the spatial heterogeneity in the electronic structures of catalysts. Therefore, a spatially resolved in-situ characterization technique is highly needed.

In a new research article published in the Beijing-based National Science Review, scientists at Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Xiamen University, University of Chinese Academy of Sciences, Imperial College London autonomously constructed an in-situ surface potential microscope and successfully measured the surface potential of the basal plane of 2H molybdenum disulfide under various voltages. This achievement addresses the experimental challenge of directly measuring the potential distribution at the non-metal/solution interface. The research findings highlight a notable difference in how the surface potential of semiconductors changes with applied voltage compared to metals. When applying voltage from positive to negative, semiconductors shift from maintaining a stable surface potential to displaying variations, gradually resembling the behavior of metals. Scientists further clarified the differences in potential drop values at various applied voltages between the semiconductor (ΔV_sem) and the double layer (ΔV_edl). They vividly explained how, in a solution environment, the semiconductor’s Fermi level and band structure evolve, demonstrating a transformation of the semiconductor into a highly conductive semimetal.

To further investigate the role of voltage in electrocatalytic reactions, scientists employed atomic force-scanning electrochemical microscopy (AFM-SECM) to study electron transfer (ET) and hydrogen evolution reaction (HER) imaging on molybdenum disulfide. In ET imaging, the semiconductor’s basal plane exhibited strong electron transfer capability, comparable to that of the semimetal edge. However, HER imaging revealed catalytic inertness at the basal plane. Nano-electrochemical imaging results indicated that voltage only affects the ET step. Due to the absence of hydrogen adsorption sites on the basal plane (i.e., chemical sites), voltage cannot drive the electrons on the basal plane to further participate in chemical reactions. This work paves the way for the rational design of efficient nonmetallic electrocatalysts based on the understanding of how voltage acts on nonmetallic catalysts at the nanoscale.

See the article:

Visualizing the role of applied voltage in non-metal electrocatalyst
Natl Sci Rev 2023; doi: 10.1093/nsr/nwad166
https://doi.org/10.1093/nsr/nwad166