Humidity Sensor of Graphite

Team Member

Xu Ruizhe, Ma Shunyu, Li Zerui, Wei Heyi

Introduction

Humidity sensors play an essential role in fields such as environmental monitoring, industrial process control, agriculture, and healthcare, where accurate humidity measurements are critical. High-quality sensors are necessary to precisely track humidity levels and variations. Traditionally, materials like metal oxides and polymers have been used, but they often suffer from limitations including slow response times, low sensitivity, and limited accuracy.

In recent years, carbon-based materials have gained significant interest due to their unique physical and chemical properties. They offer remarkable features such as high surface area, excellent electrical conductivity, and strong chemical stability across various environments. These advantages make carbon materials highly promising for developing sensitive and reliable humidity sensors. For instance, carbon nanotubes (CNTs) enhance sensor response speed thanks to their large surface area and porosity, while graphene provides ultra-fast signal transduction owing to its thin structure and superior conductivity. Other forms like carbon black and carbon fiber show strong resistance to humidity-induced degradation, ensuring long-term operational stability.

The humidity sensing capability of carbon-based materials largely stems from their interaction with water molecules. Changes in humidity cause adsorption or desorption of water on the material surface, altering properties such as resistance or capacitance. Sensor performance can further be tailored by adjusting material morphology or engineering defects.

In conclusion, carbon-based materials offer exciting prospects for future humidity sensing technologies. By continuously improving their physicochemical properties through nanotechnology and materials engineering, it is possible to develop sensors with greater sensitivity, faster response, and broader adaptability to complex humidity environments.

Literature Review

Infrared spectroscopy is an established method for the quantitative analysis of ethanol concentration due to its non-destructive, rapid, and accurate measurement capabilities. The Beer-Lambert law is commonly employed for absorbance-based concentration analysis, linking the absorbance to concentration through known absorption characteristics of molecules, although it is not directly utilized in our approach.

Previous studies have extensively explored the application of FT-IR and FT-NIR spectroscopies for ethanol measurement. Mendes et al. (2003) utilized Fourier transform near-infrared (FT-NIR) and Fourier transform Raman (FT-Raman) spectrometries combined with partial least squares (PLS) regression to determine ethanol content in fuel ethanol and beverages. Their FT-NIR model showed a relative standard error of prediction as low as 0.04%, demonstrating the technique's potential for precise ethanol quantification in complex solutions, such as beverages containing sugars (Mendes et al., 2003). Similarly, Conklin et al. (2014) demonstrated the applicability of FT-IR spectroscopy for ethanol determination in gasoline, leveraging O-H and alkane C-H absorption bands to achieve accurate ethanol measurements. Their calibration methods provided linear, reliable results consistent with actual ethanol content values, emphasizing infrared spectroscopy's robustness for quantitative chemical analysis (Conklin et al., 2014).

Advancements in spectral data analysis have significantly enhanced the accuracy and efficiency of infrared spectroscopy. Zhao and Du (2016) introduced a spectral-spatial feature-based classification (SSFC) framework combining dimension reduction and deep learning. The approach integrated balanced local discriminant embedding (BLDE) for spectral feature extraction and convolutional neural networks (CNN) for spatial feature extraction, significantly outperforming traditional spectral analysis methods in classification accuracy (Zhao & Du, 2016).

Deep learning and machine learning techniques have also emerged prominently in recent spectral feature extraction and classification studies. Wang et al. (2017) proposed a new automated spectral feature extraction method based on deep neural networks, significantly reducing computational costs compared to conventional iterative optimization algorithms. Their method successfully extracted informative and non-redundant spectral features, applicable not only to spectral classification but also defective spectra recovery (Wang et al., 2017).

These studies collectively underline the effectiveness of combining infrared spectroscopy with advanced computational techniques such as PCA and machine learning methods. PCA, particularly, has become an established technique in spectral data analysis due to its capability of reducing dimensionality while preserving significant data variance. Moreover, modern approaches such as CNN and deep learning further enhance spectral analysis by automating feature extraction and improving classification accuracy, providing an effective framework for accurately identifying ethanol concentration in complex solutions.

In short, integrating infrared spectroscopy with sophisticated computational methods offers promising avenues for precise, rapid, and non-contact ethanol concentration measurement, significantly surpassing traditional measurement techniques regarding accuracy, efficiency, and operational convenience.

Principles

Ethanol structure

Alcohol molecules exhibit distinct absorption characteristics in the infrared spectrum due to the vibrational modes of specific functional groups, particularly the hydroxyl (-OH) and methyl (-CH₃) groups. These groups interact with infrared light at specific wavelengths, leading to unique absorption peaks that can be utilized for concentration measurement.

In the near-infrared (NIR) region, the wavelength range between 1250 nm and 1350 nm is particularly sensitive to ethanol concentration. Notably, around 1300 nm, ethanol demonstrates significant variations in absorption intensity that correlate directly with its concentration. This is primarily due to the overtone and combination vibrations of the C-H and O-H bonds, which result in measurable absorption differences as ethanol content changes.

By analyzing these absorption variations, it is possible to accurately determine alcohol concentration in a given sample. The wavelength of 1300 nm is chosen as the optimal measurement wavelength because it provides a clear and distinguishable response to ethanol concentration changes.

Objective

This experiment proposes a non-contact alcohol concentration measurement method by using infrared spectroscopy. This method measures changes in infrared light intensity transmitted through alcohol solutions, using a spectrometer and principal component analysis to achieve high precision measurements.

Schematic Diagram of the Experiment

Advantages of Infrared-Based Alcohol Detection

• Non-contact measurement, reducing contamination.
• High accuracy, based on alcohol-specific infrared absorption characteristics.
• Low cost, with simple photodetection devices.

Equipment Required

• Different concentration of ethanol
• Cuvette
• LAMBDA 850 Spectrometer

Experiment Set Up

The LAMBDA 850+ UV-Visible-NIR spectrophotometer used for measurements is capable of precise spectral measurements within the wavelength range of 800 nm to 1400 nm, making it suitable for analyzing the absorption characteristics of substances such as alcohol molecules. The device is equipped with both a deuterium lamp and a tungsten-halogen lamp, providing UV and visible/near-infrared light sources, respectively. The high-quality master-engraved holographic grating monochromator ensures precise wavelength control, guaranteeing high-resolution spectral measurements.

The core of the device includes a high-sensitivity R6872 photomultiplier tube (PMT) detector, ensuring accurate signal collection across a broad wavelength range from UV to near-infrared. In the analysis of the effect of alcohol concentration changes on absorption intensity, the LAMBDA 850+ provides high sensitivity and precise measurements.

With this high sensitivity and precision configuration, it provides strong support for the optical property analysis of solutions such as alcohol, especially excelling in peak absorption location and concentration variation analysis.

Spectrometer Schematic Diagram

Experiment Procedures

• Preparation of alcohol solutions at concentrations of 25%, 50% and 75%.

Ethanol Sample Preparation

Ethanol Sample

• Measurement of absorption using spectrometer.

Spectrometer

• Use Origin software for spectral normalization and baseline correction, and process the alcohol spectra at three different concentrations.(Due to the subtle differences in the spectral data, it is difficult for non-experts to visually distinguish between the spectra of the three alcohol concentrations. Therefore, machine learning-based Principal Component Analysis (PCA) is applied for spectral classification, enabling accurate measurement of alcohol concentration.)