Browsing by Author "Ghosh, K."

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    Ionic liquid-salt based aqueous biphasic system for separation of 109Cd from silver target
    (2014) Ghosh, K.; Maiti, M.; Lahiri, S.; Afzal, Hussain, V.
    Aqueous biphasic system (ABS) is greener alternative to the conventional liquid liquid extraction as ABS does not involve any organic or volatile reagents. Generally ABS systems are composed of polymer and salt rich phases. In this paper a new ABS system is proposed replacing polymer rich phase by water soluble room temperature ionic liquid (RTIL) 1-butyl-3-methylimidazolium Chloride ([bmim]Cl) and kosmotropic salt K2HPO4. The system has been applied to separate the no-carrier-added (NCA) 109Cd (T1/2 = 462.6 days) from the ?-particle irradiated bulk Ag target. The optimum separation condition was achieved with the addition of 6 M HNO3 to the ABS, where ~87 % of the bulk Ag was extracted in the IL phase, leaving ~96 % NCA 109Cd in the salt rich phase. The salt rich phase was re-extracted twice with the RTIL to free from bulk Ag. This process achieved an overall separation of 91 % NCA 109Cd free from bulk Ag. The developed method demonstrates minimum requirement of RTIL to carry out the separation. The method is environmentally benign and cost effective. Akad miai Kiad , Budapest, Hungary 2014.
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    Ionic liquid-salt based aqueous biphasic system for separation of 109Cd from silver target
    (Kluwer Academic Publishers, 2014) Ghosh, K.; Maiti, M.; Lahiri, S.; Afzal Hussain, V.
    Aqueous biphasic system (ABS) is greener alternative to the conventional liquid liquid extraction as ABS does not involve any organic or volatile reagents. Generally ABS systems are composed of polymer and salt rich phases. In this paper a new ABS system is proposed replacing polymer rich phase by water soluble room temperature ionic liquid (RTIL) 1-butyl-3-methylimidazolium Chloride ([bmim]Cl) and kosmotropic salt K2HPO4. The system has been applied to separate the no-carrier-added (NCA) 109Cd (T1/2 = 462.6 days) from the ?-particle irradiated bulk Ag target. The optimum separation condition was achieved with the addition of 6 M HNO3 to the ABS, where ~87 % of the bulk Ag was extracted in the IL phase, leaving ~96 % NCA 109Cd in the salt rich phase. The salt rich phase was re-extracted twice with the RTIL to free from bulk Ag. This process achieved an overall separation of 91 % NCA 109Cd free from bulk Ag. The developed method demonstrates minimum requirement of RTIL to carry out the separation. The method is environmentally benign and cost effective. © Akadémiai Kiadó, Budapest, Hungary 2014.
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    SELF-PERCEPT: Introspection Improves Large Language Models’ Detection of Multi-Person Mental Manipulation in Conversations
    (Association for Computational Linguistics (ACL), 2025) Khanna, D.; Seth, P.; Murali, S.; Guru, A.; Shukla, S.; Tyagi, T.; Chaurasia, S.; Ghosh, K.
    Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipulation’s nuanced and context-specific nature, identifying manipulative language in complex, multi-turn, and multi-person conversations remains a significant challenge for large language models (LLMs). To address this gap, we introduce the MultiManip dataset, comprising 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions, all drawn from reality shows that mimic real-world scenarios. For manipulative interactions, it includes 11 distinct manipulations depicting real-life scenarios. We conduct extensive evaluations of state-of-the-art LLMs, such as GPT-4o and Llama-3.1-8B, employing various prompting strategies. Despite their capabilities, these models often struggle to detect manipulation effectively. To overcome this limitation, we propose SELF-PERCEPT, a novel, two-stage prompting framework inspired by Self-Perception Theory, demonstrating strong performance in detecting multi-person, multi-turn mental manipulation. Our code and data are publicly available at https://github.com/danushkhanna/self-percept. ©2025 Association for Computational Linguistics.