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Here, four MOFs, namely Sc-TBAPy, Al-TBAPy, Y-TBAPy, and Fe-TBAPy (TBAPy: 1,3,6,8-tetrakis(p-benzoic acid)pyrene), were characterized and evaluated for their ability to remediate glyphosate (GP) from water. Among these materials, Sc-TBAPy demonstrates superior performance in both the adsorption and degradation of GP. Upon light irradiation for 5 min, Sc-TBAPy completely degrades 100% of GP in a 1.5 mM aqueous solution. Femtosecond transient absorption spectroscopy reveals that Sc-TBAPy exhibits enhanced charge transfer character compared to the other MOFs, as well as suppressed formation of emissive excimers that could impede photocatalysis. This finding was further supported by hydrogen evolution half-reaction (HER) experiments, which demonstrated Sc-TBAPy’s superior catalytic activity for water splitting. In addition to its faster adsorption and more efficient photodegradation of GP, Sc-TBAPy also followed a selective pathway towards the oxidation of GP, avoiding the formation of toxic aminomethylphosphonic acid observed with the other M³⁺-TBAPy MOFs. To investigate the selectivity observed with Sc-TBAPy, electron spin resonance, depleted oxygen conditions, and solvent exchange with D₂O were employed to elucidate the role of different reactive oxygen species on GP photodegradation. The findings indicate that singlet oxygen (¹O₂) plays a critical role in the selective photodegradation pathway achieved by Sc-TBAPy.

 

Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy.

 

Advancements in materials discovery tend to rely disproportionately on happenstance and luck rather than employing a systematic approach. Recently, advances in computational power have allowed researchers to build computer models to predict the material properties of any chemical formula. From energy minimization techniques to machine learning-based models, these algorithms have unique strengths and weaknesses. However, a computational model is only as good as its accuracy when compared to real-world measurements. In this work, we take two recommendations from a thermoelectric machine learning model, TaVO[Formula: see text] and GdTaO[Formula: see text], and measure their thermoelectric properties of Seebeck coefficient, thermal conductivity, and electrical conductivity. We see that the predictions are mixed; thermal conductivities are correctly predicted, while electrical conductivities and Seebeck coefficients are not. Furthermore, we explore TaVO[Formula: see text]’s unusually low thermal conductivity of 1.2 Wm[Formula: see text]K[Formula: see text], and we discover a possible new avenue of research of a low thermal conductivity oxide family.

 

- Root-associated fungi (RAF) and root traits regulate plant acquisition of nitrogen (N), which is limiting to growth in Arctic ecosystems. With anthropogenic warming, a new N source from thawing permafrost has the potential to change vegetation composition and increase productivity, influencing climate feedbacks. Yet, the impact of warming on tundra plant root traits, RAF, and access to permafrost N is uncertain. - We investigated the relationships between RAF, species-specific root traits, and uptake of N from the permafrost boundary by tundra plants experimentally warmed for nearly three decades at Toolik Lake, Alaska. - Warming increased acquisitive root traits of nonmycorrhizal and mycorrhizal plants. RAF community composition of ericoid (ERM) but not ectomycorrhizal (ECM) shrubs was impacted by warming and correlated with root traits. RAF taxa in the dark septate endophyte, ERM, and ECM guilds strongly correlated with permafrost N uptake for ECM and ERM shrubs. Overall, a greater proportion of variation in permafrost N uptake was related to root traits than RAF. - Our findings suggest that warming Arctic ecosystems will result in interactions between roots, RAF, and newly thawed permafrost that may strongly impact feedbacks to the climate system through mechanisms of carbon and N cycling. 

Diffusion Models outperform Generative Adversarial Networks (GANs) and Wasserstein GANs in material discovery.

 

Low-cost self-driving labs (SDLs) offer faster prototyping, low-risk hands-on experience, and a test bed for sophisticated experimental planning software which helps us develop state-of-the-art SDLs.