Photocured polymers have recently gained tremendous interest for a wide range of applications especially industrial prototyping/additive manufacturing. This work aims to develop natural phenolic-based (meth)acrylates to expand the use of sustainable and mechanically robust 3D printable formulations.

Porosity and surface area analysis play a prominent role in modern materials science. At the heart of this sits the Brunauer–Emmett–Teller (BET) theory, which has been a remarkably successful contribution to the field of materials science. The BET method was developed in the 1930s for open surfaces but is now the most widely used metric for the estimation of surface areas of micro‐ and mesoporous materials. Despite its widespread use, the calculation of BET surface areas causes a spread in reported areas, resulting in reproducibility problems in both academia and industry. To prove this, for this analysis, 18 already‐measured raw adsorption isotherms were provided to sixty‐one labs, who were asked to calculate the corresponding BET areas. This round‐robin exercise resulted in a wide range of values. Here, the reproducibility of BET area determination from identical isotherms is demonstrated to be a largely ignored issue, raising critical concerns over the reliability of reported BET areas. To solve this major issue, a new computational approach to accurately and systematically determine the BET area of nanoporous materials is developed. The software, called “BET surface identification” (BETSI), expands on the well‐known Rouquerol criteria and makes an unambiguous BET area assignment possible.

Porosity and surface area analysis play a prominent role in modern materials science, where 123 their determination spans the fields of natural sciences, engineering, geology and medical 124 research. At the heart of this sits the Brunauer-Emmett-Teller (BET) theory,[1] which has been 125 a remarkably successful contribution to the field of materials science. The BET method was 126 developed in the 1930s and is now the most widely used metric for the estimation of surface 127 areas of porous materials.[2] Since the BET method was first developed, there has been an 128 explosion in the field of nanoporous materials with the discovery of synthetic zeolites,[3] 129 nanostructured silicas,[4–6] metal-organic frameworks (MOFs),[7] and others. Despite its 130 widespread use, the manual calculation of BET surface areas causes a significant spread in 131 reported areas, resulting in reproducibility problems in both academia and industry. To probe 132 this, we have brought together 60 labs with strong track records in the study of nanoporous 133 materials. We provided eighteen adsorption isotherms and asked these researchers to 134 calculate the corresponding BET areas, resulting in a wide range of values for each one. We 135 show here that the reproducibility of BET area determination from identical isotherms is a 136 largely ignored issue, raising critical concerns over the reliability of reported BET areas in 137 the literature. To solve this major issue, we have developed a new computational approach 138 to accurately and systematically determine the BET area of nanoporous materials. Our 139 software, called BET Surface Identification (BETSI), expands on the well-known Rouquerol 140 criteria and makes, for the first time, an unambiguous BET area assignment possible.

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Materials-based approaches are needed to achieve high volumetric density for the storage and release of hydrogen. In this work, we investigate metal oxides for their ability to store hydrogen, based on the idea that the known reduction potentials of proton-coupled electron transfer in metal oxides are in a range that suggests these materials could have suitable energetics for hydrogen storage and release at near-ambient temperature. We hypothesize that the more positive (or less negative) is the reduction potential, the greater is the favorability to absorb hydrogen. To test this idea, the absorption of atomic hydrogen (which can be derived from molecular hydrogen with a suitable catalyst) in six metal oxides (MnO 2 , MoO 3 , SnO 2 , TiO 2, WO 3 , and ZrO 2 ) is studied using density functional theory at the PBE-D3(BJ), PBE-D3(BJ)+U, and PBE0 levels of theory. A correlation between reduction potentials and the energies of absorption (relative to free H 2 ) is found, and three of the metal oxides are predicted to absorb hydrogen at storage-relevant temperatures and pressures, with MoO 3 showing the most promise.