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Metal ion linked multilayers offer a means of controlling interfacial energy and electron transfer for a range of applications including solar energy conversion, catalysis, sensing, and more. Despite the importance of structure to these interlayer transfer processes, little is known about the distance and orientation between the molecules/surface of these multilayer films. Here we gain structural insights into these assemblies using a combination of UV-Vis polarized visible attenuated total reflectance (p-ATR) and Förster Resonance Energy Transfer (FRET) measurements. The bilayer of interest is composed of a metal oxide surface, phosphonated anthracene molecule, Zn(II) linking ion, and a platinum porphyrin with one (P1), two (P2), or three (P3) phenylene spacers between the chromophoric core and the metal ion binding carboxylate group. As observed by both time-resolved emission and transient absorption, the FRET rate and efficiency decreases with an increasing number of phenylene spacers (P1 > P2 > P3). However, from p-ATR measurements we observe a change in orientation of porphyrins in the bilayer, which inhibits a uniform determination of the orientation factor (κ2) across the series. Instead, we narrow the scope of viable structures by determining the best agreement between experimental and calculated FRET efficiencies. Additionally, we provide evidence that suggests, for the first time, that the bilayer structure is similar on both planar and mesoporous substrates. 

Metal-ion-linked molecular multilayers on metal oxide surfaces are promising for applications ranging from solar energy conversion to sensing. Most of these applications rely on energy and electron transfer between layers/molecules which can be envisioned to occur via intra-assembly (IA; between metal-ion-linked molecules) and interlayer (IL; between separate layers of nonlinked molecules) processes. Here, we describe our effort to differentiate between IL and IA energy transfer using a bilayer composed of ZrO2, a phosphonated anthracene derivative (A), a zinc(II) linking ion, and a Pt(II)porphyrin (P). Both time-resolved emission and transient absorption measurements show no impact of diluting the anthracene layer with a spectroscopically inert spacer on the rate of 1A* to P and 3P* to A, singlet, and triplet energy transfer, respectively. These results indicate that energy transfer within the metal-ion-linked assembly (i.e., ZrO2-A–Zn-P) is more rapid than with an adjacent, nonlinked A molecule, even for a P derivative capable of laying down on the surface. These insights are an important step toward structural design principles maximizing the efficiency/rate of energy transfer in multilayer assemblies. 

Generative artificial intelligence (AI) technology is expected to have a profound impact on chemical education. While there are certainly positive uses, some of which are being actively implemented even now, there is a reasonable concern about its use in cheating. Efforts are underway to detect generative AI usage on open-ended questions, lab reports, and essays, but its detection on multiple choice exams is largely unexplored. Here we propose the use of Rasch analysis to identify the unique behavioral pattern of ChatGPT on General Chemistry II, multiple choice exams. While raw statistics (e.g., average, ability, outfit) were insufficient to readily identify ChatGPT instances, a strategy of fixing the ability scale on high success questions and then refitting the outcomes dramatically enhanced its outlier behavior in terms of Z-standardized out-fit statistic and ability displacement. Setting the detection threshold to a true positive rate (TPR) of 1.0, a false positive rate (FPR) of <0.1 was obtained across a majority of the 20 exams investigated here. Furthermore, the receiver operating characteristic curve (i.e., FPR vs TPR) exhibited outstanding areas under the curve of >0.9 for nearly all exams. While limitations of this method are described and the analysis is by no means exhaustive, these outcomes suggest that the unique behavior patterns of generative AI chat bots can be identified using Rasch modeling and fit statistics.