Project Se(L)ect(i)vity (PTDC/EQU-EQU/6193/2020)

The global transition to electric vehicles and renewable energy systems has heightened the demand for lithium-ion batteries (LIBs), creating an urgent need for sustainable battery recycling methods to recover critical raw materials, including lithium. Lithium-selective cation-exchange polymeric membranes are one of the emerging options to achieve such lithium recycling. To make this change even greener, instead of using traditional fossil-origin polymers to produce membranes, this research employed bacterial cellulose acetate (BCA), a bio-derived and eco-friendly polymer. By adding 5 wt% N-methyl-N-propylpiperidinium bis(trifluoromethanesulfonyl)imide (PP13–TFSI), an ionic liquid (IL) which is a plasticizer and lithium-ion conductor, and 20 wt% hydrogen manganese oxide (HMO), which is a lithium-selective inorganic filler, four BCA-based membranes (BCA, BCA-IL, BCA-HMO and BCA-IL-HMO) were prepared. The membranes were extensively characterized for their morphology, thermal stability, chemical, and mechanical properties. Subsequently, they were tested in diffusion cells (without applying any external driving force) for ionic conductivity, lithium selectivity, and lithium flux using binary salt mixtures and synthetic LIB leachate. The BCA-IL membrane outperformed other BCA-based membranes in terms of separation factors, achieving values of 10.50 (Li+/Mn2+), 11.75 (Li+/Ni2+), and 10.95 (Li+/Co2+) with a lithium flux of 0.12 mol m−2 h−1 when processing synthetic LIB leachate. Under the same conditions, the BCA-HMO membranes exhibited a higher lithium flux (0.51 mol m−2 h−1) but with lower separation factor values of 3.39 (Li+/Mn2+), 3.62 (Li+/Ni2+), and 3.36 (Li+/Co2+). The use of plant-derived cellulose acetate (CA) as an alternative to BCA was also assessed; however, despite promising ideal lithium selectivity values (for example, 112 for Li+/Ni2+ in the case of CA-HMO membrane), their conductivity was up to two orders of magnitude lower than that of BCA-based membranes. All these findings highlight the promising potential of BCA-based membranes for lithium recovery from lithium-ion battery leachates.

The rising lithium-ion battery market drives lithium demand and requires efficient and selective lithium recovery methods from aqueous sources. Membrane technologies can address environmental and inherent efficiency issues in conventional lithium extraction methods. This study presents the synthesis of novel lithium-selective mixed matrix membranes (MMMs) by integrating 0–30 wt% of a lithium selective filler named hydrogen manganese oxide (HMO) into a sulfonated polyethersulfone (SPES)-Nafion polymer matrix. The membranes were produced by casting and thoroughly examined to assess their chemical, physical, morphological, thermal, and mechanical characteristics. The transport of lithium across membranes was evaluated in diffusion and electro-diffusion studies. The membrane containing 20 wt% of HMO exhibited the highest ideal selectivity values, which were 1.05 for Li+/K+, 1.20 for Li+/Na+, and 13.36 for Li+/Mg2+; and more than 97% increase in lithium-ion conductivity when compared with the control membrane without HMO. In diffusion experiments, the binary separation factors for Li+/K+, Li+/Na+, and Li+/Mg2+ were 0.71, 1.52, and 11.83, respectively, while under electro-diffusion conditions, the corresponding values were 0.82, 1.55, and 9.88. Above 20 wt% of HMO, membranes lose their separation capacity as HMO aggregates inside the membrane structure. The higher selectivity of membranes towards Li+ in the presence of Mg2+ is due to magnesium's larger hydrated radius and higher hydration energy compared to lithium. Overall, the prepared membranes demonstrated a promising potential for green lithium recovery. This study facilitates the advancement of sustainable lithium-selective MMM synthesis.

The rising lithium-ion battery market drives lithium demand and requires efficient and selective lithium recovery methods from aqueous sources. Membrane technologies can address environmental and inherent efficiency issues in conventional lithium extraction methods. This study presents the synthesis of novel lithium-selective mixed matrix membranes (MMMs) by integrating 0–30 wt% of a lithium selective filler named hydrogen manganese oxide (HMO) into a sulfonated polyethersulfone (SPES)-Nafion polymer matrix. The membranes were produced by casting and thoroughly examined to assess their chemical, physical, morphological, thermal, and mechanical characteristics. The transport of lithium across membranes was evaluated in diffusion and electro-diffusion studies. The membrane containing 20 wt% of HMO exhibited the highest ideal selectivity values, which were 1.05 for Li+/K+, 1.20 for Li+/Na+, and 13.36 for Li+/Mg2+; and more than 97% increase in lithium-ion conductivity when compared with the control membrane without HMO. In diffusion experiments, the binary separation factors for Li+/K+, Li+/Na+, and Li+/Mg2+ were 0.71, 1.52, and 11.83, respectively, while under electro-diffusion conditions, the corresponding values were 0.82, 1.55, and 9.88. Above 20 wt% of HMO, membranes lose their separation capacity as HMO aggregates inside the membrane structure. The higher selectivity of membranes towards Li+ in the presence of Mg2+ is due to magnesium's larger hydrated radius and higher hydration energy compared to lithium. Overall, the prepared membranes demonstrated a promising potential for green lithium recovery. This study facilitates the advancement of sustainable lithium-selective MMM synthesis.

The scale-up of crystallization processes is a challenging step in production of active pharmaceutical ingredients (APIs). When moving from lab to industrial scale, the mixing conditions tend to modify due to the different geometry and agitation performance, which is particularly important in anti-solvent crystallizations where the size of the crystals depends on the mixing and incorporation of the anti-solvent in the solution. In this work, the results obtained in anti-solvent lab-scale crystallization experiments were used to develop multivariate statistical models predicting Particle Size Distribution (PSD) parameters (Dv10, Dv50 and Dv90) in function of predictors such as percentage of volume, power per volume and tip speed. Firstly, the collinearity among the predictors was assessed by Variance Inflation Factor (VIF) diagnosis. Subsequently, least squares method was employed to find correlations among the predictors and output variables. The optimization of the models was executed by testing quadratic, logarithmic and square root terms of the predictors and removing the least statistically significant regression coefficient. The quality of the fitting was evaluated in terms of adjusted R2 (R2adj). The modelled Dv10, Dv50 and Dv90 values presented a good fitting to the experimental data, with R2adj higher than 0.79, either when using power per volume or tip speed along the percentage of volume as predictors. Afterwards, the particle size distribution parameters of industrial scale production were predicted using the previously developed models. The deviations between predicted and experimental values were lower than 17%. This demonstrates that multivariate statistical models developed in lab-scale conditions can be successfully used to predict particle size distribution in industrial-size vessels.