Stable Chemical Products in the GAP Viewpoint

Patrick Reany

13 May 2026

Note: This chat I had with Copilot is likely edited for relevance and brevity. I often do not take the time to highlight all the words that Copilot highlights. Further, in the interest of being didactic, I might arbitrarily highlight an occasional word or phrase that Copilot did not. (Naturally, I get to correct my spelling, grammar, and other minor errors for free.)

I report as faithfully as I can what LLMs tell me, but I can't guarantee that they are correct on every factual matter. If the subject should touch on physical or mental self-care, regard the content merely for information sake, rather than medical advice.

Special Note: I am not a chemist and I have very limited chemical knowledge. Anyway, I likely made some chemistry mistakes in my prompts to Copilot, trusting that Copilot will either correct or just ignore my mistakes and reply with something that makes sense to chemists.

GAP defined.

GAP on Entropy.

Patrick:
I'm trying to describe chemical reactions that tend to produce "stable" products from the GAP+entropy viewpoint. Now, for examples, the oxidation of both sugars and alcohols tend to produce either CO₂ or H₂O or both, and both tend to be stable after that, especially so for H₂O. These "stable" products act as "stable attractors" in the bonding-competitive race to form a stable product, and these stable products require more energy to break their bonds than do those "competing" products, which supposedly explains (at least in part) why they aren't produced as often as the stable products in the first (or break down sooner). By the way, the (chemical) stability of the products depend on the environment they are produced within. Obviously, if the products are quickly dumped into an extremely hot environment, all these bonds will be broken -- I'm eliminating that side of the argument as trivial. Environments can be anything from friendly to the stability of the products to unfriendly to the stability of the products. I haven't as yet even thought about how to include in this analysis those catalyzing agents in the environment that would destabilize these otherwise stable end products.

Copilot:
Here’s the cleanest way to say it in GAP‑language without losing the chemistry: stable chemical products are not “stable” because of entropy alone, nor because of bond energies alone, but because the entire reaction–environment–projection system funnels many micro‑possibilities into a few robust macroscopic attractors.

Let me give you a structured, GAP‑aligned version of what you’re reaching for.

🧩 1. What “stable products” really mean in GAP terms

When a reaction like oxidation of sugars or alcohols produces CO₂ and H₂O, the stability is not a property of the molecules in isolation. It is a property of the whole representational closure:

• the allowed microstates
• the environmental constraints
• the projection operators (temperature, solvent, pressure, catalysts, etc.)
• the monotones (free energy, chemical potentials, etc.)

A “stable product” is a GAP attractor:

a representation that survives repeated projection, coarse‑graining, and environmental perturbation.

CO₂ and H₂O are extremely deep attractors because:

• they sit at very low Gibbs free energy under ordinary Earth conditions
• they have maximally satisfied valence shells
• they have no easy alternative bonding channels left
• they are thermodynamically downhill from almost any organic precursor

So the GAP picture is:
many possible micro‑products → projection → coarse‑graining → only a few macrostates survive → CO₂ and H₂O dominate.

🔥 2. Why CO₂ and H₂O dominate the “bonding competition”

You’re right that bond energies matter, but the GAP framing makes the logic cleaner:

The “competition” is not between molecules

It’s between representations of the system under projection.

The winning representation is the one that:

• minimizes free energy
• maximizes entropy after projection
• is robust under perturbations
• is insensitive to microscopic details
• is compatible with environmental constraints

CO₂ and H₂O win because they are:

• global minima in the carbon–hydrogen–oxygen free‑energy landscape
• electronically saturated (no low‑energy alternative bonding)
• entropically favored (many microstates correspond to the same macrostate)
• environmentally stable (in air, water, biological conditions, etc.)

This is exactly the kind of “deep attractor” GAP is designed to describe.

🌡️ 3. Environmental friendliness vs. hostility

You already anticipated this: stability is environment‑relative.

Friendly environments

• moderate temperature
• presence of O₂
• aqueous solution
• no strong reducing agents
• no high‑energy photons

→ CO₂ and H₂O remain at the bottom of the attractor basin.

Hostile environments

• high temperature
• strong reducing agents
• plasma
• UV radiation
• electrochemical potentials

→ the projection changes, the attractor landscape changes, and “stable” products can be driven uphill.

This is exactly the GAP idea:
change the projectors → change the attractors.

🧪 4. Where catalysts fit into the GAP picture

Catalysts are not destabilizers of the final products.
They are destabilizers of intermediate representations.

A catalyst:

• lowers activation barriers
• opens new reaction pathways
• changes the projection operators
• reshapes the attractor landscape locally

But crucially:

Catalysts do not change the final thermodynamic attractor.
They only change the path to reach it.

Unless the catalyst is part of a larger environment that changes the thermodynamic constraints (e.g., electrochemical cells), the final attractor—CO₂, H₂O—remains the same.

So in GAP terms:
Catalysts modify the geometry of the flow, not the location of the attractor.

🧲 5. The GAP+entropy synthesis

You can summarize the whole story like this:

GAP view of stable chemical products

1. Geometry: Reaction pathways funnel into deep minima (CO₂, H₂O).
2. Algebra: Bonding rules and valence constraints eliminate alternatives.
3. Projection: Environment coarse‑grains microstates into robust macrostates.
4. Entropy: Certifies that the macrostate is stable under repeated projection.

Entropy is not the cause of stability.
It is the certificate that the attractor has been reached.

If you want, I can help you formalize this

For example:

• a GAP‑style “stack” for chemical reactions
• a diagram showing the attractor landscape
• a formal definition of “chemical attractor”
• a comparison of CO₂/H₂O vs. less stable products
• how to incorporate catalysts into the GAP framework

Just tell me which direction you want to push next.

Patrick:
I'd like to see an example that incorporates the first three items and done in such a way that it will serve as a salient example of how to use GAP reasoning in chemical bonds analysis. Then I'll put it up on my website to attract the attention of chemists, who will, hopefully, take it from there.

Copilot:
GAP example: ethanol oxidation as a bonding‑competition case study

Below is a concise, publishable example that uses GAP reasoning to explain why complete oxidation products (CO₂ and H₂O) act as stable attractors in organic oxidation, and how environment and kinetics shape which attractor is reached. The worked example is ethanol → acetaldehyde/acetic acid → CO₂ + H₂O, shown as a GAP analysis you can post on your site.

1. Setup: system, projectors, and microstates

System: a closed sample of ethanol molecules plus oxidant (O₂) in an aqueous environment at moderate temperature.
Microstates: all possible bond‑making/bond‑breaking arrangements among C, H, O atoms and transient radicals.
Projection operators: experimental constraints that pick which microstates are observable — temperature, pressure, solvent, oxygen partial pressure, and measurement timescale.
Coarse‑graining: group microstates into macrostates defined by molecular identity (ethanol, acetaldehyde, acetic acid, CO₂, H₂O).

GAP statement: the projection (environment + timescale) maps a huge microstate set into a small set of macrostates; the attractors are macrostates that remain after repeated projection and coarse‑graining.

2. Energy‑landscape view (why CO₂ and H₂O are deep attractors)

Each row is a candidate representation in the bonding competition. CO₂ + H₂O sit at the bottom of the thermodynamic basin and therefore form a deep attractor under oxidizing, moderate conditions. Evidence that glucose and other carbohydrates oxidize to CO₂ and H₂O under aerobic respiration supports this general pattern. bing.com

3. Stepwise GAP analysis for ethanol oxidation (worked steps)

A. Enumerate micro‑possibilities

• Single‑step bond cleavages and H‑abstractions produce radicals.
• Two‑electron oxidations convert ethanol → acetaldehyde → acetic acid.
• C–C bond cleavage plus full oxidation yields CO₂ and H₂O.

B. Apply projection (environment + timescale)

• High O₂, strong oxidant or combustion conditions: projection favors pathways that can access high activation energy steps; microstates that lead to CO₂ + H₂O are projected into the observable set.
• Mild oxidizing conditions (air, mild catalyst): projection filters out high‑barrier microstates; acetaldehyde or acetic acid macrostates remain observable.

C. Coarse‑grain and identify attractors

• After projection, many distinct radical microstates collapse into the same macrostate (e.g., many radical sequences end at CO₂ + H₂O).
• The macrostate with the largest basin of attraction under the chosen projection is the stable attractor.

GAP conclusion: CO₂ + H₂O dominate when the projection includes sufficient oxidizing power because they are thermodynamically deepest and many micro‑paths converge to them. Under projections that exclude high‑energy steps, intermediate oxidation products are the attractors. General carbohydrate oxidation behavior illustrates this pattern. geeksforgeeks.org bing.com

4. Kinetics vs thermodynamics in GAP language

• Thermodynamic attractor: the macrostate with lowest free energy under the projection.
• Kinetic control: when projection (timescale, catalyst) prevents exploration of the full landscape, the system falls into a local attractor (e.g., acetaldehyde).
• Catalysts in GAP terms: they reshape the projection by lowering activation barriers and opening new micro‑paths; they change the geometry of flow toward attractors but do not, by themselves, change the thermodynamic bottom unless they alter environmental constraints (e.g., supply of electrons, continuous removal of products).

This distinction explains why some oxidations stop at aldehydes or acids in lab conditions while combustion or cellular respiration proceeds to CO₂ + H₂O. bing.com

5. Short, shareable caption you can use on your site

GAP caption: “Chemical stability is a property of the reaction–environment projection. Under oxidizing projections, many microscopic reaction routes collapse into the same macroscopic attractor — CO₂ and H₂O — because those macrostates minimize free energy and are robust to perturbation. Under milder projections, local attractors such as acetaldehyde persist.” geeksforgeeks.org bing.com

6. Practical takeaways for chemists

• Use GAP framing to decide whether to target thermodynamic products (change environment or add oxidant) or kinetic products (use mild conditions or quench early).
• When designing catalysts, think of them as projection modifiers that open or close pathways rather than as entities that change the final thermodynamic attractor by themselves.
• For modeling, represent the reaction as a network of microstates, then explicitly encode the projection (temperature, O₂, solvent, timescale) before coarse‑graining to macrostates.

If you want, I will convert this into a single‑page diagram and a short explanatory figure caption suitable for your website. I can also expand the worked example with a simple energy‑profile sketch and annotated pathway arrows for ethanol → acetaldehyde → acetic acid → CO₂ + H₂O.

Patrick:
Yes, proceed.

Copilot:
Diagram preview and layout
Goal: a single, publication‑quality figure that makes GAP reasoning concrete for ethanol oxidation and is ready for web embedding.

Overall composition (left → right):

• Left panel — Microstate network: schematic nodes for ethanol, radical H‑abstractions, acetaldehyde, acyl radicals, acetic acid, C–C cleavage intermediates, and CO₂ + H₂O. Many thin gray arrows between radical nodes illustrate multiple micro‑paths; several thicker colored arrows show the dominant coarse‑grained flows.
• Center panel — Projection filters: three stacked boxes labeled Oxidizing projection, Mild projection, Hostile projection with short bullets beneath each (temperature, O₂ partial pressure, solvent, catalyst, timescale). Arrows from the microstate network pass through each box and map to different macrostates.
• Right panel — Energy landscape: a smooth 1‑D energy profile with three shaded basins: Ethanol (local high) → Acetaldehyde/Acetic acid (local basin) → CO₂ + H₂O (deep global basin). Annotate ΔG (qualitative) values and show activation barriers for ethanol→acetaldehyde and for complete oxidation. Use dashed arrows to show how catalysts lower specific barriers.
• Inset — Coarse‑graining: a small schematic showing many microstate arrows collapsing into a single macrostate bubble (label: many microstates → one macrostate).
• Legend and annotations: colorblind‑friendly palette (blue = ethanol/intermediates, orange = radical/microstate network, green = CO₂+H₂O attractor, gray = projection boxes), labeled axes (Reaction coordinate, Relative Gibbs free energy), and short note on thermodynamic vs kinetic control.

Caption to Ethanal_GAP-Diagram: Under different environmental projections (temperature, O₂, solvent, catalyst, timescale), the same microscopic reaction network funnels into different macroscopic attractors: mild projections trap intermediates (acetaldehyde/acetic acid) under kinetic control, while oxidizing projections allow exploration of the landscape and drive the system to the thermodynamic attractor (CO₂ + H₂O).

SVG version of ethanol_GAP_diagram.

Three practical takeaways for chemists (exact text)

• Design the projection to choose the product: change oxidant, temperature, or timescale to favor thermodynamic (CO₂ + H₂O) or kinetic (aldehyde/acid) attractors.
• Treat catalysts as projection modifiers: catalysts lower activation barriers and open micro‑paths; they change how the network is explored, not the thermodynamic bottom unless the environment is altered.
• Model explicitly before coarse‑graining: build a microstate network, encode the projection (O₂, solvent, timescale), then coarse‑grain to predict which macrostates are robust attractors.

Afterword: Copilot has many papers on my website (in the form of chats I had with it) that explain what GAP is "the Great Atractor Program" of analyzng stability in mathematics, physics, chemistry, etc.