Portable and sensitive mixed-potential type solid-state electrolyte (MPSE) gas sensors can detect exhaled biomarkers in a noninvasive and inexpensive way, which is significant for convenient disease diagnosis and saving medical resources. However, high working temperature is still one of the main bottlenecks for hindering MPSE gas sensors’ applications in disease diagnosis. Here, we, for the first time, developed and fabricated new room-temperature MPSE gas sensors utilizing K2Fe4O7 electrolyte and Ni/Fe–MOF (Ni/Fe clusters are coordinated with 1,4-H2BDC) sensing electrodes (SEs) for the detection of ppb-level NO. Among different MOF SEs, the sensor attached with the Ni–MOF SE presents the highest NO sensitivities. This is attributed to a reducing oxygen reduction reaction activity and enhancing NO electrochemical catalytic reaction activity, verified by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) tests. In addition, the presented sensor also shows a low detection limit (20 ppb), fast response/recovery characteristic (17 s/6 s to 50 ppb NO), excellent selectivity, acceptable repeatability, and long-term stability of 34 days to NO at 25 °C and 60%RH. Simultaneously, the mechanism of humidity effect on the sensing performance was investigated by EIS and CV tests. Our work provides new insight into the development of room-temperature solid-state electrolyte gas sensors based on the mixed-potential mechanism and enlarges the potential application domain.

A sensitive, selective, and stable electrochemical sensor based on Mn 1-x Zn x Fe 2 O 4 (x = 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0) nanoparticle and Nafion-modified glassy carbon electrode (GCE) was designed and developed to detect Pb 2+ . The uniform and monodisperse Mn 1-x Zn x Fe 2 O 4 nanospheres were synthesized via a hard template-free (soft template) hydrothermal method. This research mainly focused on the influence of different Zn 2+ substituting ratios in Mn 1-x Zn x Fe 2 O 4 on sensing characteristics of the sensor. The highest response value to 0.6 μM Pb 2+ was observed for the sensor using Mn 0.4 Zn 0.6 Fe 2 O 4 . In addition, the influence of experimental parameters (e.g., the kinds of electrolyte, pH, deposition potential, and time) on sensing performance was studied. When measured in 0.1 M NaAc-HAc (pH = 2.0) at the deposition potential of −1.0V with the deposition time of 130 s, Mn 1-x Zn x Fe 2 O 4 and Nafionmodified GCE exhibited good sensitivity of 58.613 μA/μM, favorable repeatability, and an ultralow detection limit of 0.7 nM (based on S/N ratio = 3). The superior sensing properties to Pb 2+ were attributed to the bigger electrochemically effective surface area with the addition of Zn 2+ , high adsorption capacity, and high specific surface area of Mn 0.4 Zn 0.6 Fe 2 O 4 nanospheres. Using Nafion also enhanced the adsorption capacity and stability of the modified electrode.

Yttrium-stabilized zirconia (YSZ)-based mixed potential-type NO x sensors have broad application prospects in automotive exhaust gas detection. Great efforts continue to be made in developing high-performance sensitive electrode materials for mixed potential-type NO2 gas sensors. However, only five kinds of new sensing electrode materials have been developed for this type of gas sensor in the last 3 years. In this work, four different tree-based machine learning models were trained to find potentially sensitive electrode materials for NO2 detection. More than 400 materials were selected from 8000 materials by the above machine learning models. To further verify the reliability of the model, 13 of these materials containing unexploited elements were selected as sensitive electrode materials for making sensors and testing their gas-sensing performances. The experimental results showed that all 13 materials exhibited good gas-sensing performance for NO2. More interestingly, an electrode material BPO4, which does not contain any metal elements, was also screened out and showed good sensing properties to NO2. In a short period of time, 13 new sensitive electrode materials for NO2 detection were targeted and screened, which was difficult to achieve by a trial-and-error procedure.