Using <i>i</i> -3D to visualize porphyrin normal deformation modes: A tutorial on porphyrin conformational analysis
Bibliographic record
Abstract
An understanding of porphyrin conformations is crucial for comprehending how nature regulates the electronic properties of porphyrinic prosthetic groups. The non-planar conformations of synthetic porphyrins are frequently also the key to their utility as NIR absorbers, chiral entities, or organocatalysts. The well-established normal-structure-deformation (NSD) analysis of porphyrins deconvolutes the conformation of any porphyrin quantitatively into six normal out-of-plane modes (and in-plane distortion modes, which are ignored here). NSD analyses are now readily accessible via online tools. However, complete comprehension of the output of a porphyrin conformational NSD analysis requires an understanding of the porphyrin deformation modes, including their origin and implications. Alas, the primary literature is, for most students new to porphyrin chemistry, unsuitable to serve as an introduction to NSD/porphyrin conformational analysis. Moreover, students often struggle to perceive chemical structures in 3D. Utilizing GIF and [Formula: see text]-3D technologies, we thus provide a primer on porphyrin conformational analysis, some fundamental factors that can induce the deformation of porphyrins, and the consequences of porphyrin non-planarity. [Formula: see text]-3D technology allows graphical interaction with porphyrin crystal structures and was previously shown to assist students in the visualization and understanding of the three-dimensionality of chemistry. This tutorial fills the instructional gap between what students learn in the classroom, what is available in the primary literature, and the online tools that allow even a novice to execute an NSD analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.030 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".