After downloading and going through the paper and skimming through the supplementary info, I felt this is very ideal topic and this paper a great seed data for the following reasons:
First and foremost the Glassy State of Ionic liquid is a hot and growingly popular research topic owing to various application in self-healing, shape memory system, and other functional properties it can exhibit.
Second, I have been paying attention to this area of research for quite some time now (almost 6 years) since the time I am working on Perovskite and other advanced material solution under different solvents, trying to model their non-Newtonian behavior and coating processability. So I am aware of this literature and know there are abundance of research data / prior framework dating back to many outstanding Prof. De Gennes work in the 80s and 90s.
Third, this particular paper is very meticulous in its reporting giving almost as much data as possible (see screenshot below for the type of data) which going to be very helpful for our ARG (Autonomous Research Group, A Group of Highly Specialized AI-Agents doing Research) to start their investigation. Moreover the data are available in both text and visual (image and video) which is very beneficial in honing and testing the Multi-Modal capabilities of the ARGs.
Thus in general the theme and approach for this work will be very appropriate for the topic 2 in the use-case I am trying to develop. The fact that the solvent cross-linking that led to this glassy gel nature are primarily driven by various ionic bond and solute-solvents interaction, which can be modeled (as shown in paper too) this will be a nice way for the ARG (which currently can yet do experiment in real life) to perform their research in theoretical-computational means, through developing a simple model to mimic the system and do analysis on it. That will be a great feedback loop so the ARG research work could be based on real data calculated and simulated properly and not just some hallucinative numbers obtained from "next-word predictions" of the LLM that drives the communication between the AI Agents involved.
ps: Since the main paper from Nature is not open-access and thus can't be uploaded here, those who want to read the main paper and don't have access to it, feel free to reach out for personal copy.
Details from here: https://www.researchhub.com/user/993107/discussions
After downloading and going through the paper and skimming through the supplementary info, I felt this is very ideal topic and this paper a great seed data for the following reasons:
First and foremost the Glassy State of Ionic liquid is a hot and growingly popular research topic owing to various application in self-healing, shape memory system, and other functional properties it can exhibit.
Second, I have been paying attention to this area of research for quite some time now (almost 6 years) since the time I am working on Perovskite and other advanced material solution under different solvents, trying to model their non-Newtonian behavior and coating processability. So I am aware of this literature and know there are abundance of research data / prior framework dating back to many outstanding Prof. De Gennes work in the 80s and 90s.
Third, this particular paper is very meticulous in its reporting giving almost as much data as possible (see screenshot below for the type of data) which going to be very helpful for our ARG (Autonomous Research Group, A Group of Highly Specialized AI-Agents doing Research) to start their investigation. Moreover the data are available in both text and visual (image and video) which is very beneficial in honing and testing the Multi-Modal capabilities of the ARGs.
Thus in general the theme and approach for this work will be very appropriate for the topic 2 in the use-case I am trying to develop. The fact that the solvent cross-linking that led to this glassy gel nature are primarily driven by various ionic bond and solute-solvents interaction, which can be modeled (as shown in paper too) this will be a nice way for the ARG (which currently can yet do experiment in real life) to perform their research in theoretical-computational means, through developing a simple model to mimic the system and do analysis on it. That will be a great feedback loop so the ARG research work could be based on real data calculated and simulated properly and not just some hallucinative numbers obtained from "next-word predictions" of the LLM that drives the communication between the AI Agents involved.
ps: Since the main paper from Nature is not open-access and thus can't be uploaded here, those who want to read the main paper and don't have access to it, feel free to reach out for personal copy.
Best, Domi.