As the most abundant natural polymer, cellulose presents a unique advantage for large-scale applications. To fully unlock its potential, the introduction of desired functional groups onto the cellulose backbone is required, which can be realized by either chemical bonding or physical surface interactions. This review gives an overview of the chemistry behind the state-of-the-art functionalization methods (e.g., oxidation, esterification, grafting) for cellulose in its various forms, from nanocrystals to bacterial cellulose. The existing and foreseeable applications of the obtained products are presented in detail, spanning from water purification and antibacterial action, to sensing, energy harvesting, and catalysis. A special emphasis is put on the interactions of functionalized cellulose with heavy metals, focusing on copper as a prime example. For the latter, its toxicity can either have a harmful influence on aquatic life, or it can be conveniently employed for microbial disinfection. The reader is further introduced to recent sensing technologies based on functionalized cellulose, which are becoming crucial for the near future especially with the emergence of the internet of things. By revealing the potential of water filters and conductive clothing for mass implementation, the near future of cellulose-based technologies is also discussed.not suitable for drinking water treatment in rural areas or not applicable on a large scale. Other methods involving materials which have high adsorption capacities for pollutants, for example activated charcoal have been explored. [27,28] Nevertheless, there are no universal adsorbents for all pollutants, meaning that superior alternatives are still required. In recent years, biodegradable adsorbents such as cellulose and chitosan have attracted much attention for the removal of heavy metals.In particular cellulose has a number of advantages, which include high abundance, low cost, easy access, nontoxicity and biodegradability. It is particularly suited for the removal of low concentrations of heavy metals which occur in contaminated ground or tap water. [26] Beyond the aspects of copper removal from water, in this review we indicate how the metal's increased toxicity for microbial life can present an advantage for medical applications. Moreover, we also take a closer look at further applications of functionalized cellulose, including catalysis and sensing, which can make a great impact regarding energy conversion and management. The inclusion of conductive cellulose sensors within the internet of things global network is of particular interest, since an accurate weather forecast can provide swift adjustments in the usage of the often intermittent renewable energy sources. In order to understand what confers functionalized cellulose its versatility for broad applications, we first exemplify the functionalization techniques on a molecular basis.

We provide spectroscopic and computational evidence for a substantial change in structure and gas phase reactivity of Al3O4 + upon Fe-substitution, which is correctly predicted by multireference (MR) wave function calculations. Al3O4 + exhibits a cone-like structure with a central trivalent O atom (C3v symmetry). The replacement of the Al- by an Fe atom leads to a planar bicyclic frame with a terminal Al–O•– radical site, accompanied by a change from the Fe+III/O–II to the Fe+II/O–I valence state. The gas phase vibrational spectrum of Al2FeO4 + is exclusively reproduced by the latter structure, which MR wave function calculations correctly identify as the most stable isomer. This isomer of Al2FeO4 + is predicted to be highly reactive with respect to C–H bond activation, very similar to Al8O12 + which also features the terminal Al–O•– radical site. Density functional theory, in contrast, predicts a less reactive Al3O4 +-like “isomorphous substitution” structure of Al2FeO4 + to be the most stable one, except for functionals with very high admixture of Fock exchange (50%, BHLYP).

A comprehensive and diverse benchmark set for the calculation of 29 Si NMR chemical shifts is presented. The SiS146 set includes 100 silicon containing compounds with 146 experimentally determined reference 29 Si NMR chemical shifts measured in nine different solvents in a range from −400 to +828 ppm. Silicon atoms bound to main group elements as well as transition metals with coordination numbers of 2−6 in various bonding patterns including multiple bonds and coordinative and aromatic bonding are represented. The performance of various common and specialized density functional approximations including (meta-)GGA, hybrid, and double-hybrid functionals in combination with different AO basis sets and for differently optimized geometries is evaluated. The role of scalar-relativistic effects is further investigated by inclusion of the zeroth order regular approximation (ZORA) method into the calculations. GGA density functional approximations (DFAs) are found to outperform hybrid DFAs with B97-D3 performing best with an MAD of 7.2 ppm for the subset including only light atoms (Z < 18), while TPSSh is the best tested hybrid functional with an MAD of 10.3 ppm. For 29 Si cores in the vicinity of heavier atoms, the application of ZORA proved indispensable. Inclusion of spin−orbit effects into the 29 Si NMR chemical shift calculation decreases the mean absolute deviations by up to 74% compared to calculations applying effective core potentials.

A new benchmark set termed SnS51 for assessing quantum chemical methods for the computation of 119Sn NMR chemical shifts is presented. It covers 51 unique 119Sn NMR chemical shifts for a selection of 50 tin compounds with diverse bonding motifs and ligands. The experimental reference data are in the spectral range of ±2500 ppm measured in seven different solvents. Fifteen common density functional approximations, two scalar- and one spin–orbit relativistic approach are assessed based on conformer ensembles generated using the CREST/CENSO scheme and state-of-the-art semiempirical (GFN2-xTB), force field (GFN-FF), and composite DFT methods (r2SCAN-3c). Based on the results of this study, the spin–orbit relativistic method combinations of SO-ZORA with PBE0 or revPBE functionals are generally recommended. Both yield mean absolute deviations from experimental data below 100 ppm and excellent linear regression determination coefficients of ≤0.99. If spin–orbit calculations are not affordable, the use of SR-ZORA with B3LYP or X2C with ωB97X or M06 may be considered to obtain qualitative predictions if no severe spin–orbit effects, for example, due to heavy nuclei containing ligands, are expected. An empirical linear scaling correction is demonstrated to be applicable for further improvement, and respective empirical parameters are given. Conformational effects on chemical shifts are studied in detail but are mostly found to be small. However, in specific cases when the ligand sphere differs substantially between conformers, chemical shifts can change by up to several hundred ppm.

NMR spectroscopy undoubtedly plays a central role in determining molecular structures across different chemical disciplines, and the accurate computational prediction of NMR parameters is highly desirable. In this work, a new Δ-machine learning approach is presented to correct DFT-computed NMR chemical shifts using input features from the calculation and in addition highly accurate reference data at the CCSD(T)/pcSseg-2 level of theory with a basis set extrapolation scheme. The model is trained on a data set containing 1000 optimized and geometrically distorted structures of small organic molecules comprising most elements of the first three periods and containing data for 7090 1H and 4230 13C NMR chemical shifts. Applied to the PBE0/pcSseg-2 method, the mean absolute deviation (MAD) on the internal NMR shift test set is reduced by 81% for 1H and 92% for 13C at virtually no additional computational cost. For 12 different DFT functional and basis set combinations, the MAD of the ML-corrected NMR shifts ranges from 0.021 to 0.039 ppm (1H) and from 0.38 to 1.07 ppm (13C). Importantly, the new method consistently outperforms the simple and widely used linear regression correction technique. This behavior is reproduced on three different external benchmark sets, confirming the generality and robustness of the correction scheme, which can easily be applied in DFT-based spectral simulations.