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The current study proposes a low-cost, environmentally benign manufacturing approach of copper ferrite nanoparticles (CuFe2O4 NPs) via mushroom extract of Pleurotus florida (PFE) as the first-time report. Several characterization methods verified the production of PFE-CuFe2O4 NPs. The absorption spectrum exhibited the peak at 420 nm; band gap of 1.85 eV. The studies of SEM and TEM confirmed spherical and homogeneously distributed CuFe2O4 NPs with an average size of 22.4 ± 1.4 nm. The FTIR reported the presence of bio-essential molecules in PFE can act as a stabilizing and capping agent. The NPs were found to be fairly stable with zeta potential found at 28.9 ± 0.2 mV. Numerous in vitro biological investigations exemplified the applicability and practicality of CuFe2O4 NPs and compare them with the standard. The biofunctionalized CuFe2O4 NPs demonstrated a potent antibacterial activity against E. coli and S. aureus. Additionally, it was discovered that CuFe2O4 NPs have superior antioxidant activity (77–83%) and their scavenging ability is more comparable to ascorbic acid (control). Furthermore, a degradation efficiency of 91–92% was observed in 10–15 min for CuFe2O4 NPs in rhodamine B (RhB) and methylene blue (MB) dyes, indicating their remarkable effectiveness in this regard. Future research may focus on applying CuFe2O4 NPs to comprehensive wastewater treatment and determining the degradation products and ecological consequences.

Conversational agents designed for emotionally supportive interactions face challenges in balancing affective responsiveness, computational efficiency, and safety in communication. Prior approaches frequently depend on large-scale models, handcrafted affective objectives, or reinforcement learning from human feedback, which can limit scalability and interpretability. This work presents a lightweight, domain-adapted dialogue generation system based on the T5-small architecture, fine-tuned on MentalChat16K, a curated corpus of real and synthetic emotional-support conversations. The proposed model operates without reinforcement learning or emotion-specific training objectives, yet demonstrates encouraging alignment with affective cues and fluent response generation within the evaluated dataset. Empirical evaluation shows improvements over zero-shot and fine-tuned GPT-2 baselines, achieving BLEU (32.14), ROUGE-L (44.72), and BERTScore-F1 (85.11). Expert human assessments indicated high ratings in coherence, emotional appropriateness, and contextual relevance, with substantial inter-rater agreement. Qualitative error analysis indicated generally conservative and context-aware responses within the evaluated sample. During manual review of this sample, no factual hallucinations, medical overreach, or overtly unsafe responses were observed; however, systematic safety benchmarking was beyond the scope of the present study. This study provides initial evidence that compact transformer-based models, when adapted to domain-specific corpora and evaluated under controlled conditions, can support efficient and affectively appropriate dialogue generation in emotionally supportive non-clinical settings, while requiring further safety validation before broader real-world deployment.