📄 chemistry

Eco-Friendly Carbon Quantum Dots for Fluorescent Sensing of Amoxicillin Coupled with Machine Learning and DOE

This study presents an eco-friendly carbon quantum dot-based fluorescent sensor for amoxicillin detection, optimized via response surface methodology and enhanced by machine learning algorithms to achieve high sensitivity, selectivity, and predictive accuracy in complex matrices.

Original authors: Diba Adami, Darya Barbod, Sara Bijari, Changiz Karami

Published 2026-06-25

📖 4 min read☕ Coffee break read

CC BY 4.0

Original authors: Diba Adami, Darya Barbod, Sara Bijari, Changiz Karami

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: A "Glow-in-the-Dark" Antibiotic Detector

Imagine you have a magical flashlight that shines a specific color of light. When you shine this light on a clean glass of milk, it glows brightly. But, if there is a hidden "intruder" in the milk—specifically, the antibiotic Amoxicillin—the light suddenly dims or "quenches."

This paper describes a team of scientists who built a new, eco-friendly version of this magical flashlight using tiny particles called Carbon Quantum Dots (CQDs). They didn't just build it; they used a computer "recipe book" (Machine Learning and Experimental Design) to make sure it works perfectly.

1. Making the "Magic Dust" (Green Synthesis)

Instead of using harsh chemicals or expensive lab equipment to make these tiny particles, the researchers used a plant called Quercus infectoria (a type of oak tree).

The Analogy: Think of this like making tea. They boiled the dried leaves in water to get an extract. Then, they mixed this "plant tea" with a carbon source and cooked it under high pressure (like a pressure cooker) for 24 hours.
The Result: This process created billions of tiny, spherical specks (about 3 nanometers wide—so small you'd need a super-microscope to see them). Because they came from a plant, they are "green" and eco-friendly.
What they found: Using high-tech microscopes (TEM and SEM), they confirmed these specks are round, uniform, and covered in helpful chemical groups (like oxygen and hydroxyl groups) that act like "sticky hands" to grab onto the antibiotic.

2. The "Recipe" for Perfection (Design of Experiments & Machine Learning)

The scientists knew that to get the best "dimming" effect, they needed to get the conditions just right. They had four main knobs to turn:

pH: How acidic or basic the mixture is.
Time: How long they let the antibiotic and the dots mix.
Temperature: How hot the mixture was.
Dosage: How much "magic dust" they added.

The Strategy:

The "Taste Test" (DOE): Instead of guessing, they used a statistical method called Response Surface Methodology (RSM). Imagine a chef trying to find the perfect soup recipe. They don't just guess; they test specific combinations of salt, heat, and time to see which one tastes best. The computer analyzed 30 different "recipes" to find the sweet spot.
The "Smart Coach" (Machine Learning): They also trained a computer program (called Random Forest) to look at all the data and predict the best outcome.
The Winner: The computer found that Time and pH were the most important factors. Surprisingly, Temperature didn't matter much within the range they tested. The "Random Forest" computer model was actually better at predicting results than the standard math formulas, proving that AI can help scientists fine-tune their experiments.

3. How It Works: The Light Dimming Trick

When the researchers mixed their plant-made dots with Amoxicillin, the dots stopped glowing as brightly.

The Analogy: Imagine the dots are fireflies. When Amoxicillin is present, it's like a heavy blanket being thrown over the fireflies, making them glow less. The more Amoxicillin there is, the darker the glow becomes.
The Measurement: By measuring exactly how much the light dimmed, they could calculate exactly how much antibiotic was in the sample.

4. Is It Reliable? (Testing the Sensor)

The team put the sensor through a rigorous "stress test":

Selectivity (The "Look-Alike" Test): They tested if other things in milk (like calcium, sugar, vitamins, or other antibiotics) would trick the sensor.
- Result: The sensor was very picky. It only reacted strongly to Amoxicillin. The other ingredients barely changed the light, meaning the sensor won't give false alarms.
Real-World Test (The "Milk Challenge"): They took real milk from a local supermarket in Iran, added known amounts of Amoxicillin, and tried to detect it.
- Result: The sensor found the antibiotic almost perfectly (recovering about 99–101% of what was added). It worked just as well in messy real milk as it did in clean water.

5. The Bottom Line

The paper concludes that they have created a sensitive, cheap, and eco-friendly tool to detect Amoxicillin in dairy products.

Why it matters: Antibiotics in milk can cause allergies and resistance in bacteria. This sensor offers a way to check milk quickly without needing expensive, toxic chemicals.
The "Superpower": By combining plant-based materials with smart computer modeling, they created a sensor that is robust, accurate, and ready to help keep our food safe.

In short: They turned oak leaves into glowing sensors, used a computer to find the perfect recipe, and proved it can sniff out hidden antibiotics in milk without getting confused by anything else.

Technical Summary: Eco-Friendly Carbon Quantum Dots for Fluorescent Sensing of Amoxicillin Coupled with Machine Learning and DOE

Problem Statement
The extensive and often uncontrolled use of amoxicillin, a widely used β-lactam antibiotic, poses significant risks to public health and the environment. Residual amoxicillin in dairy products, particularly milk, can enter the human food chain, potentially causing allergic reactions and contributing to antibiotic resistance. Furthermore, its release into ecosystems through animal waste threatens water and soil quality. While fluorescent quantum dots (QDs) have emerged as powerful tools for detecting pollutants due to their high intensity and photostability, there is a need for cost-effective, environmentally friendly synthesis methods and robust analytical frameworks that can handle complex matrices like milk.

Methodology
This study developed a sensitive and selective fluorescent sensor for amoxicillin detection using Carbon Quantum Dots (CQDs) synthesized via a green, plant-based approach.

Synthesis and Characterization: CQDs were synthesized using Quercus infectoria extract via hydrothermal treatment at 180°C for 24 hours. The resulting nanomaterials were comprehensively characterized using Transmission Electron Microscopy (TEM), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX) with elemental mapping and line scans, Fourier Transform Infrared Spectroscopy (FTIR), and fluorescence spectroscopy.
Experimental Design (DOE): To optimize the sensing conditions, a Central Composite Design (CCD) under Response Surface Methodology (RSM) was employed. Four factors were evaluated: pH (6–8), temperature (25–45°C), incubation time (2–5 min), and sensor dosage (30–50 µg). The response variable was the fluorescence quenching ratio ( $F_0/F$ ).
Machine Learning Integration: To enhance predictive accuracy, machine learning models, specifically Random Forest and Support Vector Regression (SVR), were trained on the experimental data and compared against quadratic regression models.
Detection Protocol: The sensor operates on a fluorescence quenching mechanism. Upon mixing the CQD solution with amoxicillin-containing samples (including spiked raw milk), fluorescence intensity is measured at an excitation wavelength of 320 nm and emission at 425 nm.
Sample Preparation: Real milk samples were processed using trichloroacetic acid precipitation, sonication, and centrifugation to remove matrix interferences before analysis.

Key Results

Material Properties: The synthesized CQDs exhibited a uniform spherical morphology with an average diameter of approximately 3 nm. Elemental analysis confirmed a carbon-oxygen backbone (56.9% C, 34.5% O) with trace phytochemical elements (K, Ca, Si, Mg, Na, S). FTIR analysis identified surface functional groups including hydroxyl, carbonyl, and ether/ester groups, which are critical for the sensing mechanism.
Optimization and Statistical Modeling: The quadratic model derived from the CCD showed excellent adequacy ( $R^2 = 0.9834$ , Adjusted $R^2 = 0.9680$ ). ANOVA revealed that reaction time, pH, and sensor dosage were the most statistically significant factors influencing fluorescence quenching, while temperature had a negligible effect within the tested range. Diagnostic plots confirmed the model's validity regarding linearity, normality, and homoscedasticity.
Machine Learning Performance: The Random Forest model demonstrated superior predictive capability compared to the quadratic regression model, achieving an $R^2$ of approximately 0.96 with lower error metrics (RMSE and MAE), highlighting the effectiveness of ensemble learning in capturing nonlinear multivariate interactions.
Analytical Performance: The sensor established two linear calibration ranges: 10–300 µM and 300–650 µM. The limits of detection (LOD) were determined to be 41.47 µM and 101.78 µM for the respective ranges, with limits of quantification (LOQ) of 125.66 µM and 308.43 µM. The method showed high precision with a relative standard deviation (RSD) of 3.39% for blanks.
Selectivity and Application: Interference studies indicated negligible effects from common ions (Ca²⁺, K⁺, Na⁺) and biomolecules (lactose, casein, vitamins, organic acids). When applied to real dairy samples spiked with amoxicillin, the sensor achieved recovery rates between 98.9% and 101.0% with RSDs below 3.2%, demonstrating robustness in complex matrices.

Significance and Claims
The paper claims that the integration of green-synthesized CQDs, Design of Experiments (DOE) for optimization, and Machine Learning evaluation creates a robust platform for amoxicillin detection. While the authors acknowledge that the detection limits (41.47 µM) are higher than some previously reported nanosensors (which achieved sub-microgram levels), they emphasize the unique advantages of their system:

Broad Dynamic Range: The sensor covers a wide concentration window (10–650 µM), making it suitable for monitoring higher antibiotic residues where narrow-range sensors might fail.
Eco-Friendliness: The use of Quercus infectoria aligns with green chemistry principles, offering a sustainable alternative to toxic chemical precursors.
Practical Utility: The method requires minimal pretreatment and maintains high accuracy in real dairy matrices, positioning it as a viable tool for routine food safety monitoring in traditional and artisanal dairy products.

The study concludes that this approach offers a sensitive, selective, and scalable solution for antibiotic surveillance, contributing to improved food safety and public health monitoring without the need for extensive sample preparation.