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Numerous scientific, health care, and industrial applications are showing increasing interest in developing optical pH sensors with low-cost, high precision that cover a wide pH range. Although serious efforts, the development of high accuracy and cost-effectiveness, remains challenging. In this perspective, we present the implementation of the machine learning technique on the common pH paper for precise pH-value estimation. Further, we develop a simple, flexible, and free precise mobile application based on a machine learning algorithm to predict the accurate pH value of a solution using an available commercial pH paper. The common light conditions were studied under different light intensities of 350, 200, and 20 Lux. The models were trained using 2689 experimental values without a special instrument control. The pH range of 1: 14 is covered by an interval of ~ 0.1 pH value. The results show a significant relationship between pH values and both the red color and green color, in contrast to the poor correlation by the blue color. The K Neighbors Regressor model improves linearity and shows a significant coefficient of determination of 0.995 combined with the lowest errors. The free, publicly accessible online and mobile application was developed and enables the highly precise estimation of the pH value as a function of the RGB color code of typical pH paper. Our findings could replace higher expensive pH instruments using handheld pH detection, and an intelligent smartphone system for everyone, even the chef in the kitchen, without the need for additional costly and time-consuming experimental work.

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Low-cost lightweight geopolymer mortars based on water-cooled slag, fly ash, and silica sand flour were prepared as a structural and thermally insulating material. The effect of chemical foaming agents such as hydrogen peroxide (H2O2) and sodium perborate tetrahydrate (NaBO3·4H2O) on thermal conductivity, bulk density, water absorption, porosity, and compressive strength was studied. FTIR, XRD, XRF, and SEM were used to investigate the raw materials and selected samples of prepared lightweight geopolymers. The prepared lightweight geopolymers were given a compressive strength of 1.05 to 17 MPa. The compressive strength, bulk density, and thermal conductivity values decrease with increasing foaming agent content due to the decomposition of its chemical structure and releasing of oxygen bubbles. The results show that hydrogen peroxide mixes have better performance in the physio-chemical and thermal properties than sodium perborate mixes to achieve low thermal conductivity (0.21–0.24 W/mK) with compressive strength values (1.18–3.45 MPa) for MS-H1 and MS-H2 mixes, respectively. According to the results of bulk density (454–800 kg/m3), MS-H1, MS-H2, MS-B3, and MS-B4 mixes can be considered ultra-lightweight. Using silica sand flour in powder form improves the physicochemical and thermal properties of the lightweight geopolymer and decreases the production cost of the lightweight geopolymers.