Alkaline phosphatase functionalized \nnanoparticles: attachment, enzyme \nkinetics and colloidal diffusion
Bibliographic record
Abstract
Enzymes are proteins found in organisms that work as biological catalysts. In the \nlast decade, some studies report that enzymes diffuse faster during catalysis [1], \nand recently others show the possibility of making enzyme-powered micromotors; \nwhere urease-functionalized microparticles appear to diffuse faster during catalysis \nas observed by trajectory tracking [2]. We studied the validity of using the enzyme \nalkaline phosphatase as a nanomotor on spherical polystyrene particles with a diameter \nof 200 nm (attached by glutaraldehyde coupling) using differential dynamic \nmicroscopy (DDM) and dynamic light scattering (DLS) to obtain the diffusion coefficient \nof those particles compared to bare particles of the same size looking for any \nenhanced particles motion. We will report on the existence (or absence) of enhancement \nin diffusivity. The enzyme activity of our alkaline phosphatase functionalized \nnanoparticles, obtained by spectroscopy and the Michaelis-Menten relation [3], was \nfound to be very similar (slightly lower) to the bare alkaline phosphatase activity. \nDDM is a technique that exploits optical microscopy to obtain local quantitative \ninformation about dynamic samples (diffusion coefficient, particle size) by probing \nwave vector-dependent dynamics [4]. DDM could be used to study the dynamics \nin liquid suspensions, soft materials, cells, and tissues. In DDM, image sequences \nare analyzed via a combination of image differences and spatial Fourier transforms \nto obtain information equivalent to that obtained by means of dynamic light scattering \n(DLS) techniques. Compared to DLS and particle trajectory tracking, DDM \noffers obvious advantages, most importantly, providing high statistics by capturing a \nlarge number of particles, removing the static contributions along the optical path, \nflexibility of choosing an analysis region, and the power of simultaneous different \nmicroscopy contrast mechanisms. But those advantages come with a price; it is \nchallenging to know the suitable settings (camera speed, objective magnification, \nsample dilution, etc.) for each measurement (i.e., each particle size). In order to \nvalidate our DDM setup, we studied a range of polystyrene particles size (60 nm-1 \nmicron) suspended in water using different settings to conclude the suitable settings \nfor each size in that range. Using previously published Python code [5] and modified \nby our group, we managed to analyze thousands of frames (images) with the \nspeed of hundreds of frames per second for each measurement. All measurements \nwere compared to DLS measurements on the same samples (but more diluted) for \ncomparison.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".