As a follow up to yesterday’s note and continuing my obsession with AI and it’s evaluation of the fire hazard of lab chemicals, here’s today’s entry from Google’s Bard:
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Is DMF more of a fire hazard than acetone?

"Yes, DMF is more of a fire hazard than acetone. The flash point of DMF is 136°F (57°C), while the flash point of acetone is -40°F (-40°C). This means that DMF can ignite and burn at a lower temperature than acetone. Additionally, DMF is more volatile than acetone, meaning that it evaporates more easily. This makes it more likely to form an ignitable mixture with air.

"Here is a table comparing the fire hazards of DMF and acetone:
Property DMF Acetone
Flash point 136°F (57°C) -40°F (-40°C)
Autoignition temperature 800°F (427°C) 760°F (400°C)
Lower flammability limit 4.1% 2.5%
Upper flammability limit 13.4% 7.9%
Vapor pressure 10 mmHg at 20°C 21 mmHg at 20°C
Density 0.95 g/cm3 0.79 g/cm3

"As you can see, DMF has a lower flash point, autoignition temperature, and lower flammability limits than acetone. This means that it is more likely to ignite and burn. Additionally, DMF has a higher vapor pressure than acetone, which means that it evaporates more easily. This makes it more likely to form an ignitable mixture with air.

"Therefore, DMF is more of a fire hazard than acetone. It is important to use caution when handling DMF and to take steps to reduce the risk of fire.”

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It appears that the first paragraph contradicts itself and that all of the interpretive statements in the third paragraph contradict the data presented in the table. I wonder if other people get similar responses from ChatBots or if I should take their responses personally? ;)

I will note that yesterday that Google announced their next AI based product (currently code-named Tailwind) which is described thusly:
"On the Tailwind team we’ve been referring to our general approach as source-grounded AI. Language models, as we all know, have a habit of hallucinating facts, and while the information contained in the model’s training data is vast, it is also one-size-fits-all. Tailwind allows you to define a set of documents as trusted sources which the AI then uses as a kind of ground truth, shaping all of the model’s interactions with you. In the use case shown on the I/O stage, the sources are class notes, but it could be other types of sources as well, such as your research materials for a book or blog post. The idea here is to craft a role for the LLM that is not an all-knowing oracle or your new virtual buddy, but something closer to an efficient research assistant, helping you explore the information that matters most to you.”

This product could be available for testing this summer.

- Ralph

Ralph Stuart, CIH, CCHO
ralph**At_Symbol_Here**rstuartcih.org

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