Development and Application of Magnesium Citrate-Functionalized Starch-Based Nanomaterials in Enhancing the Fortification of Vitamin D<sub>3</sub>: Batch and Release Performance Studies
Bibliographic record
Abstract
Recently, diverse strategies have been developed to formulate food micronutritional supplements like magnesium (Mg 2+ ) and vitamin D 3 (VD 3 ). Nevertheless, the progress in food supplement formulations is limited, due to their undesirable interactions with various food ingredients. In the last few decades, starch has been widely used to prepare micro or nano-carriers for different micronutrients following diverse physical and chemical methods, due to its excellent surface features, the possibility of derivation from various reagents, biodegradability, and biocompatibility. However, most of these methods have not yet been tested in the laboratory scales, failed partially or completely in producing uniform particles in nanoscale domains, and require multiple formulation steps. Herein, starch nanomaterials (SNMs) are fabricated as an effective carrier for VD 3 and Mg 2+ by ultrasonication under moderate conditions. Initially, the SNMs were prepared by ultrasonication using equal masses of native corn starch (NCS) and high amylose corn starch (HACS). The generated materials were then grafted with magnesium, in citrate form at room conditions, as a primary functionalization step, forming magnesium citrate-grafted SNM (MNM-Mg), which were used as sorbents for VD 3 . Experimentally, a set of analytical methods including atomic force microscopy (AFM), Brunauer–Emmett–Teller (BET) surface area analysis, Fourier-transform infrared spectroscopy (FT-IR), Zeta sizer, and X-ray diffraction analysis (XRD) were used to determine the size, surface properties, functionality, stability, and morphology of the prepared nanomaterials. The adsorptive behavior of VD 3 on MNM-Mg surfaces was investigated by analyzing the equilibrium adsorptive data using various isotherm models including Langmuir, BET, Toth, and Redlich–Peterson. Furthermore, the release kinetics for the MNM-Mg after adsorbing VD 3 (MNM-Mg-VD 3 ) were tested in a phosphate buffer solution at pH 7.4, mimicking human bloodstream conditions. Our results showed the successful synthesis of stable MNM-Mg (Z potential of −36 mV) with an estimated average size of 10 nm and a BET surface area of 28 m 2 /g. To the best of our knowledge, the VD 3 that was loaded on our primarily modified nanomaterials with magnesium citrate, compared with the physically mixed VD 3 MNM-Mg with VD 3 (MNM-Mg+VD 3 (PM)) and directly administrated, tended to be completely released after 5 h with lower diffusivity and greater controlled release performance.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".