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posted by mrpg on Friday January 18 2019, @01:30PM   Printer-friendly
from the don't-lose-hope-humans! dept.

First green leaf on moon dies as temperatures plummet

The appearance of a single green leaf hinted at a future in which astronauts would grow their own food in space, potentially setting up residence at outposts on the moon or other planets. Now, barely after it had sprouted, the cotton plant onboard China’s lunar rover has died.

The plant relied on sunlight at the moon’s surface, but as night arrived at the lunar far side and temperatures plunged as low as -170C, its short life came to an end.

Prof Xie Gengxin of Chongqing University, who led the design of the experiment, said its short lifespan had been anticipated. “Life in the canister would not survive the lunar night,” Xie said.


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  • (Score: 2) by maxwell demon on Friday January 18 2019, @07:11PM (1 child)

    by maxwell demon (1608) on Friday January 18 2019, @07:11PM (#788371) Journal

    We know quite well how to create that sort of light here on Earth, in order to see how plants react on it. Indeed, as Planck spectrum, it is one of the easiest spectra to replicate.

    When doing an experiment, you want to change as few parameters as possible. On the moon, you have the unique opportunity to check the parameter setting "strength of gravity = g/6" on your plant growth. You wouldn't want that result spoiled by an additional spectrum of light parameter change.

    --
    The Tao of math: The numbers you can count are not the real numbers.
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  • (Score: 0) by Anonymous Coward on Saturday January 19 2019, @05:42PM

    by Anonymous Coward on Saturday January 19 2019, @05:42PM (#788702)

    When doing an experiment, you want to change as few parameters as possible.

    That is a common misconception and almost always a waste of limited resources but for the most trivial experiments. Experiment optimization is a much more elaborated process than to brute force your way in.

    What you really want is to map the space of the experiment parameters to the results and then find the optimum with the less possible number of test and for that usually you need to change all parameters at once.

    There is lot of technical literature for the math inclined about experiment optimization, and even an good introductory MOOC Experimentation for Improvement [coursera.org].