physics.ins-det

arXiv:2501.10385v1 Announce Type: new Abstract: The emergence of large language models (LLMs) has accelerated the development of self-driving laboratories (SDLs) for materials research. Despite their transformative potential, current SDL implementations rely on rigid, predefined protocols that limit their adaptability to dynamic experimental scenarios across different labs. A significant challenge persists in measuring how effectively AI agents can replicate the adaptive decision-making and experimental intuition of expert scientists. Here, we introduce AILA (Artificially Intelligent Lab Assistant), a framework that automates atomic force microscopy (AFM) through LLM-driven agents. Using AFM as an experimental testbed, we develop AFMBench-a comprehensive evaluation suite that challenges AI agents based on language models like GPT-4o and GPT-3.5 to perform tasks spanning the scientific workflow: from experimental design to results analysis. Our systematic assessment shows that state-of-the-art language models struggle even with basic tasks such as documentation retrieval, leading to a significant decline in performance in multi-agent coordination scenarios. Further, we observe that LLMs exhibit a tendency to not adhere to instructions or even divagate to additional tasks beyond the original request, raising serious concerns regarding safety alignment aspects of AI agents for SDLs. Finally, we demonstrate the application of AILA on increasingly complex experiments open-ended experiments: automated AFM calibration, high-resolution feature detection, and mechanical property measurement. Our findings emphasize the necessity for stringent benchmarking protocols before deploying AI agents as laboratory assistants across scientific disciplines.

arXiv:2501.00476v1 Announce Type: new Abstract: This paper implies Bluetooth technology, which is put into effect to alter extant, wired into wireless Programmable Logic Controller (PLC). Here two Bluetooth devices are employed as a transceiver to transmit and receives the input signal to contrive wireless PLC. The main advantage of PLC is to control the output according to the status of input. In Bluetooth technology, the handshaking between the two Bluetooth modules takes place, which is interfaced with a microcontroller board (Arduino board) and then to PLC such that field devices can be controlled without wire.