Facile Assembly of Large-Area 2D Microgel Colloidal Crystals Using Charge-Reversible Substrates
Bibliographic record
Abstract
2D colloidal crystals (CCs) have important applications; however, the fabrication of large-area, high-quality 2D CCs is still far from being trivial, and the fabrication of 2D microgel CCs is even harder. Here, we have demonstrated that they can be facilely fabricated using charge-reversible substrates. The charge-reversible substrates were prepared by modification with amino groups. The amino groups were then protected by amidation with 2,2-dimethylsuccinic anhydride. At acidic pH, the surface charge of the modified substrate will change from negative to positive as a result of the hydrolysis of the amide bonds and the regeneration of the amino groups. 2D microgel CCs can be simply fabricated by applying a concentrated microgel dispersion on the modified substrate. The negatively charged surface of the substrate allows the negatively charged microgel spheres, especially those close to the substrate, to self-assemble into 3D CCs. With the gradual hydrolysis of the amide bonds and the charge reversal of the substrate, the first 111 plane of the 3D assembly is fixed in situ on the substrate. The resulting 2D CC has a high degree of ordering because of the high quality of the parent 3D microgel CC. Because large-area 3D microgel CCs can be facilely fabricated, this method allows for the fabrication of 2D CCs of any size. Nonplanar substrates can also be used. In addition, the interparticle distance of the 2D array can be tuned by the concentration of the microgel dispersion. Besides rigid substrates (such as glass slides, quartz slides, and silicon wafers), flexible polymer films, including polyethylene terephthalate and poly(vinyl chloride) films, were also successfully used as substrates for the fabrication of 2D microgel CCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".