Fiber Core-Offset Humidity Sensor Based on Graphene Oxide Characteristics

Abstract

On the basis of the photoelectric properties and hydrophilicity and multimode interference of Graphene Oxide (GO), this study proposes an all-fiber humidity sensor. Two core-offset regions are constructed with a fiber fusion splicer, and a GO film is coated on a single-mode fiber (SMF) between the core-offset regions to form a humidity-sensitive Mach-Zehnder interference structure. As the external humidity environment changes, the refractive index of the GO changes, and the light in the SMF is modulated by humidity. External humidity is measured by detecting the changes in the wavelength of the transmission spectrum of the sensing structure. A saturated salt solution is used to build a stable humidity environment. The sensitivity of the sensor is 0.05 nm/%relative humidity (RH) at 35.3%-95.8% RH, and the linear relationship between the characteristic wavelength and RH is 88%. In addition, the linear relationship between the light intensity and the RH of the sensor at a single wavelength at 1571 nm is analyzed, and the linearity is 97.45%. According to this finding, photoelectric conversion is used to convert the light intensity signal into a voltage signal. The data are processed by a computer to achieve the real-time and visual monitoring of the humidity environment. The fiber optic humidity sensor proposed in this work has a simple structure, simple production, low cost, and high sensitivity. This work provides a new method for humidity sensing.

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

“…Based on this estimation, statistical evaluation of these models is tabulated in Based on this comparison, it can be observed that different models are useful for different application requirements. For instance, in terms of accuracy as observed from figure 5, DBN RBM [2], CRNS [3], SMAP RF DN [19], GOFCHS [27], TDR [28], and P Band & L Band [34] models outperform other models, thus, they can be used for highly accurate moisture detection applications. Similarly, cost of deployment & computational complexity is visualized from figure 6, wherein it is observed that HPCM [6], HF RFID TFS [9], PWM [10], PMMA [15], FFCSM [16], MHPS [21], ECT [24], PQCWC [25], and HSAAA [32] require lowest deployment cost, while HPCM [6], PHS [17], ECT [24], and PQCWC [25] have lower computational complexity when compared with other models.…”

“…These models utilize high-speed sensors, which might be costly, but provide quicker results when compared with other sensing models. Similarly, scalability of these models is also evaluated, which indicates that CM [11] has the highest scalability, which is followed by DBN RBM [2], SMAP [18], SMAP RF DN [19], GOFCHS [27], SAR [29], SMI MODIS [30], SSMDI [33], P Band & L Band [34], and MSNs [39] models.…”

“…Based on this ARS value, it can be observed that ECT [24], PQCWC [25], MHPS [21], FoS [20], HPCM [6], PHS [17], HF RFID TFS [9], PMMA [15], FFCSM [16], HSAAA [32], PWM [10], FTO [35], CRNS [3] and CM [11] have better overall performance, thus can be used for efficient moisture sensing applications. LEWS LR [1] LR RBM [2] CRNS [3] MWMS [5] GPS [7] HF RFID TFS [9] CM [11] PLMR [12] MSR [14] FFCSM [16] SMAP [18] FoS [20] PRS [22] PQCWC [25] GOFCHS [27] SAR [29] CSMOS [31] SSMDI [33] FTO [35] PBG [38] MSOCCML [40] CRNS [42] Accuracy of moisture sensing models…”

“…The model performance is limited in terms of accuracy, but can be used for approximating moisture levels for a wide variety of applications. To improve this performance, work in [26,27,28] proposes use of Internet of Trees (IoTr) with low cost & highly dispersive electromagnetic sensors (HDES), Graphene oxide for design of fibre core-offset humidity sensor (GOFCHS), and Time Domain Reflectometry (TDR) for low error, and high-accuracy moisture level estimations. These models utilize various machine learning techniques to improve quality of detection, but require high performance computational processors for effective implementation & control.…”

IoT and mobile app-based food spoilage alert system

“…The humidity of the outside atmosphere is measured by detecting adjustments withinside the wavelength of the sensing structure's transmission spectrum. [8].…”