The use of lignocellulosic biomass in the chemical industry has increased due to its non-competition with the food sector. Several platform molecules can be produced from this biomass. Among them, levulinic acid and its esters have been produced on an industrial scale. There are some reviews on the production of levulinic acid (LA) but few on the production of levulinates (LEs). To fill this gap, this review was written by also considering the environmental aspects. In the first stage, recent progress in the production of these platform chemicals was discussed. Production processes of alkyl levulinates from levulinic acid esterification, precursors (HMF, furfural, etc.), sugar monomers (glucose, fructose, etc.), cellulose, hemicellulose, or cellulose directly from lignocellulosic biomass were described. In the second stage, process separation and purification of LA and LEs were discussed. The final stage proposed an economic and environmental consideration for the production of these chemicals.

Computational approaches have evolved as efficient alternatives to understand the adverse effects of nanoparticles on human health and the environment. The potential of using Quantitative Structure–Activity Relationship (QSAR) modeling to establish statistically significant models for predicting the cytotoxicity of various metal oxide (MeOx) nanoparticles (NPs) has been investigated. A novel kind of nanospecific theoretical descriptor was proposed by integrating codes of certain physicochemical features into SMILES-based optimal descriptors to characterize the nanostructure information of NPs. The new descriptors were then applied to model MeOx NP cytotoxicity to both Escherichia coli bacteria and HaCaT cells for comparison purposes. The effects of size variation on the cytotoxicity to both types of cells were also investigated. The four resulting QSAR models were then rigorously validated, and extensively compared to other previously published models. The results demonstrated the robustness, validity and predictivity of these models. Predominant nanostructure factors responsible for MeOx NP cytotoxicity were identified through model interpretation. The results verified different mechanisms of nanotoxicity for these two types of cells. The proposed models can be expected to reliably predict the cytotoxicity of novel NPs solely from the newly developed descriptors, and provide guidance for prioritizing the design and manufacture of safer nanomaterials with desired properties.

Since the application of nanomaterials has expanded as a therapeutic methodology, the cytotoxicity accompanying nanomaterials during the therapeutic process has attracted significant attention. Thus, it is necessary to investigate the relationship between the structure and cytotoxicity of different nanomaterials. To avoid case-by-case testing, intelligent strategies combining testing methods with non-testing predictive modelling should be developed. The quantitative structure–activity relationship (QSAR) is a promising tool in understanding the properties that affect the potency of metal oxide (MeOx) nanoparticles (NPs) and in predicting toxic responses. In this work, a combined experimental and computational study was performed to estimate the acute cytotoxicity and develop predictive models for MeOx NPs. Improved SMILES-based optimal descriptors were applied to describe the nanostructure characteristics of MeOx NPs. Based on the experimental test, four nano-QSAR models were established for predicting the median lethal concentration (LC₅₀) of MeOx NPs to human lung adenocarcinoma (A549) cells by considering the influence of their particle size and zeta potential on cytotoxicity. The models showed satisfactory predictivity and robustness. The predominant nanostructure characteristics and the mechanism responsible for the cytotoxicity of MeOx NPs to A549 cells were identified through model interpretation with subsequent experiments on the reactive oxygen species (ROS) of MeOx NPs. The proposed models can reliably predict and assess the acute cytotoxicity of novel NPs solely from their nanostructures, and provide guidance for prioritising the design, synthesis, and manufacture of safer and green nanomaterials with expected properties.