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Metal halide perovskites based on formamidinium (FA), or FA‐rich compositions have shown great promise for high‐performance photovoltaics. A deeper understanding of the impact of ambient conditions (e.g., moisture, oxygen, and illumination) on the possible reactions of FA‐based perovskite films and their processing sensitivities has become critical for further advances toward commercialization. Herein, we investigate reactions that take place on the surface of the FA_0.7Cs_0.3, mixed Br/I wide bandgap perovskite thin films in the presence of humid air and ambient illumination. The treatment forms a surface layer containing O, OH, and N‐based anions. We propose the latter originates from formamidine trapped at the perovskite/oxide interface reacting further to cyanide and/or formamidinate—an understudied class of pseudohalides that bind to Pb. Optimized treatment conditions improve photoluminescence quantum yield owing to both reduced surface recombination velocity and increased bulk carrier lifetime. The corresponding perovskite solar cells also exhibit improved performance. Identifying these reactions opens possibilities for better utilizing cyanide and amidinate ligands, species that may be expected during vapor processing of FA‐based perovskites. Our work also provides new insights into the self‐healing or self‐passivating of MA‐free perovskite compositions where FA and iodide damage could be partially offset by advantageous reaction byproducts.

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Characterization of fungal spider pathogens lags far behind their insect counterparts. In addition, little to nothing is known concerning the ecological reservoir and/or fungal entomopathogen community surrounding infection sites. Five infected spider cadavers were identified in the neo-tropical climate of north-central Florida, USA, from three of which viable cultures were obtained. Multi-locus molecular phylogenetic and morphological characterization identified one isolate as a new Gibellula species, here named, Gibellula floridensis, and the other isolates highly similar to Parengyodontium album. The fungal entomopathogen community surrounding infected spiders was sampled at different habitats/trophic levels, including soil, leaf litter, leaf, and twig, and analyzed using ITS amplicon sequencing. These data revealed broad but differential distribution of insect-pathogenic fungi between habitats and variation between sites, with members of genera belonging to Metarhizium and Metacordyceps from Clavicipitaceae, Purpureocillium and Polycephalomyces from Ophiocordyceps, and Akanthomyces and Simplicillium from Cordycipitaceae predominating. However, no sequences corresponding to Gibellula or Parengyodontium, even at the genera levels, could be detected. Potential explanations for these findings are discussed. These data highlight novel discovery of fungal spider pathogens and open the broader question regarding the environmental distribution and ecological niches of such host-specific pathogens.

 

In this full research paper, we discuss the benefits and challenges of using GPT-4 to perform qualitative analysis to identify faculty’s mental models of assessment. Assessments play an important role in engineering education. They are used to evaluate student learning, measure progress, and identify areas for improvement. However, how faculty members approach assessments can vary based on several factors, including their own mental models of assessment. To understand the variation in these mental models, we conducted interviews with faculty members in various engineering disciplines at universities across the United States. Data was collected from 28 participants from 18 different universities. The interviews consisted of questions designed to elicit information related to the pieces of mental models (state, form, function, and purpose) of assessments of students in their classrooms. For this paper, we analyzed interviews to identify the entities and entity relationships in participant statements using natural language processing and GPT-4 as our language model. We then created a graphical representation to characterize and compare individuals’ mental models of assessment using GraphViz. We asked the model to extract entities and their relationships from interview excerpts, using GPT-4 and instructional prompts. We then compared the results of GPT-4 from a small portion of our data to entities and relationships that were extracted manually by one of our researchers. We found that both methods identified overlapping entity relationships but also discovered entities and relationships not identified by the other model. The GPT-4 model tended to identify more basic relationships, while manual analysis identified more nuanced relationships. Our results do not currently support using GPT-4 to automatically generate graphical representations of faculty’s mental models of assessments. However, using a human-in-the-loop process could help offset GPT-4’s limitations. In this paper, we will discuss plans for our future work to improve upon GPT-4’s current performance. 

In this full research paper, we discuss the benefits and challenges of using GPT-4 to perform qualitative analysis to identify faculty’s mental models of assessment. Assessments play an important role in engineering education. They are used to evaluate student learning, measure progress, and identify areas for improvement. However, how faculty members approach assessments can vary based on several factors, including their own mental models of assessment. To understand the variation in these mental models, we conducted interviews with faculty members in various engineering disciplines at universities across the United States. Data was collected from 28 participants from 18 different universities. The interviews consisted of questions designed to elicit information related to the pieces of mental models (state, form, function, and purpose) of assessments of students in their classrooms. For this paper, we analyzed interviews to identify the entities and entity relationships in participant statements using natural language processing and GPT-4 as our language model. We then created a graphical representation to characterize and compare individuals’ mental models of assessment using GraphViz. We asked the model to extract entities and their relationships from interview excerpts, using GPT-4 and instructional prompts. We then compared the results of GPT-4 from a small portion of our data to entities and relationships that were extracted manually by one of our researchers. We found that both methods identified overlapping entity relationships but also discovered entities and relationships not identified by the other model. The GPT-4 model tended to identify more basic relationships, while manual analysis identified more nuanced relationships. Our results do not currently support using GPT-4 to automatically generate graphical representations of faculty’s mental models of assessments. However, using a human-in-the-loop process could help offset GPT-4’s limitations. In this paper, we will discuss plans for our future work to improve upon GPT-4’s current performance. 

Allosteric control of reaction thermodynamics is well understood, but the mechanisms by which changes in local geometries of receptor sites lower activation reaction barriers in electronically uncoupled, remote reaction moieties remain relatively unexplored. Here we report a molecular scaffold in which the rate of thermal E-to-Z isomerization of an alkene increases by a factor of as much as 10⁴in response to fast binding of a metal ion to a remote receptor site. A mechanochemical model of the olefin coupled to a compressive harmonic spring reproduces the observed acceleration quantitatively, adding the studied isomerization to the very few reactions demonstrated to be sensitive to extrinsic compressive force. The work validates experimentally the generalization of mechanochemical kinetics to compressive loads and demonstrates that the formalism of force-coupled reactivity offers a productive framework for the quantitative analysis of the molecular basis of allosteric control of reaction kinetics. Important differences in the effects of compressive vs. tensile force on the kinetic stabilities of molecules are discussed.

 

Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e.g., due to flaws in the training objective and data. We present empirical evidence of inverse scaling on 11 datasets collected by running a public contest, the Inverse Scaling Prize, with a substantial prize pool. Through analysis of the datasets, along with other examples found in the literature, we identify four potential causes of inverse scaling: (i) preference to repeat memorized sequences over following in-context instructions, (ii) imitation of undesirable patterns in the training data, (iii) tasks containing an easy distractor task which LMs could focus on, rather than the harder real task, and (iv) correct but misleading few-shot demonstrations of the task. We release the winning datasets at https://inversescaling.com/data to allow for further investigation of inverse scaling. Our tasks have helped drive the discovery of U-shaped and inverted-U scaling trends, where an initial trend reverses, suggesting that scaling trends are less reliable at predicting the behavior of larger-scale models than previously understood. Overall, our results suggest that there are tasks for which increased model scale alone may not lead to progress, and that more careful thought needs to go into the data and objectives for training language models.