Water hydrogen bonding (H‐bonding) to α‐helical transmembrane (TM) peptides is fundamental to better understand the behavior and function of α‐helical peptides, disease pathways, and the development of new drugs. Deep‐UV resonance Raman (dUVRR) spectroscopy is a non‐destructive technique amenable to both lipophilic and aqueous environments, which is an excellent and convenient approach for studying water H‐bonding (or water accessibility) to α‐helical TM peptides in a membrane mimicking environment. The dUVRR results indicate that water molecules can access the lipid membrane and form H‐bonds with carbonyl groups along α‐helical backbones. Raman bands at ~1,629 and ~1,672 cm−1 can be used to monitor the hydration and dehydration conditions along TM α‐helices. Two bands at ~1,300 and ~1,340 cm−1 are also potential characteristic features of the dehydration and hydration along the α‐helices in a membrane environment.

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One of the solutions to deal with water crisis problems is using agricultural residue capabilities as low-cost and the most abundant adsorbents for the elimination of pollutants from aqueous media. This research assessed the potential of activated carbon obtained from rice husk (RHAC) to eliminate caffeine from aqueous media. For this, the impact of diverse parameters, including initial caffeine concentration (C0), RHAC dosage (Cs), contact time (t), and solution pH, was considered on adsorption capacity. The maximum caffeine uptake capacity of 239.67 mg/g was obtained under the optimum conditions at an RHAC dose of 0.5 g, solution pH of 6, contact time of 120 min, and initial concentration of 80 mg/L. The best fit of adsorption process data on pseudo-first-order kinetics and Freundlich isotherm indicated the presence of heterogeneous and varying pores of the RHAC, multilayer adsorption, and adsorption at local sites without any interaction. Additionally, modeling the adsorption by using statistical and mathematical models, including classification and regression tree (CART), multiple linear regression (MLR), random forest regression (RFR), Bayesian multiple linear regression (BMLR), lasso regression (LR), and ridge regression (RR), revealed the greater impact of C0 and Cs in predicting adsorption capacity. Moreover, the RFR model performs better than other models due to the highest determination coefficient (R2 = 0.9517) and the slightest error (RMSE = 2.28).