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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.

 

This work utilizes the collection of Raman spectra directly from thin layer chromatography (TLC) plates for quantitative determination of the pigment content of plant leaves.

 

Metal–organic frameworks (MOFs) have emerged as a highly tunable class of porous materials with wide-ranging applications from gas capture to photocatalysis. Developing these exciting properties to their fullest extent requires a thorough mechanistic understanding of the structure–function relationships. We implement an ultrafast spectroscopic toolset, femtosecond transient absorption and femtosecond stimulated Raman spectroscopy (FSRS), to elucidate the correlated electronic and vibrational dynamics of two isostructural 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy)-based MOFs, which manifest drastically different photocatalytic behaviors. Systematic comparisons between the M3+-TBAPy MOFs and bare ligands in various environments reveal the unproductive dimer formation in Al-TBAPy, whereas Sc-TBAPy is dominated by a catalytically active charge-transfer (CT) process. Two ground-state FSRS marker bands of the TBAPy ligand at ∼1267 and 1617 cm−1 probe the chromophore environment at thermal equilibrium. For comparison, the excited-state FSRS of Sc-TBAPy suspended in neutral water unveils a key ∼300 fs twisting motion of the TBAPy peripheral phenyl groups toward planarity, promoting an efficient generation of CT species. This motion also exhibits high sensitivity to solvent environment, which can be a useful probe; we also showed the CT variation for ultrafast dynamics of Sc-TBAPy in the glyphosate aqueous solution. These new insights showcase the power of table-top tunable FSRS methodology to delineate structural dynamics of functional molecular systems in action, including MOFs and other photosensitive “nanomachines.” We expect the uncovered ligand motions (ultrafast planarization) to enable the targeted design of new MOFs with improved CT state characteristics (formation and lifetime) to power applications, including photocatalysis and herbicide removal from waterways. 

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.

 

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