Zinc oxide (ZnO) nanocomposite sensors decorated with various dopants are popular tools for detecting even low hydrogen (H) concentrations. The nanocomposite's chemistry, temperature, and H concentration impact the success of hydrogen sensors. Extensive laboratory-scale studies were conducted to investigate the effect of these variables on sensor performance, there is currently no model to relate the nanocomposite's sensitivity to its influential variables. This study proposes a stacked model by integrating Extra tree and XGBoost (eXtreme Gradient Boosting) regressor to precisely relate the sensitivity of the ZnO-based sensor to the nanocomposite's chemistry, H concentration, and temperature. The model's accuracy is superior to that of conventional artificial neural networks, achieving outstanding prediction results with mean absolute error (MAE) = 0.11, mean squared error (MSE) = 0.31, mean absolute percentage error (MAPE) = 1.14%, and R-squared (R) = 0.9994 based on 208 actual sensor sensitivities. Also, the designed stacked model predicts 206 experimental records with relative error ranges from −4% to 8%. Applicability domain analysis confirms the validity of almost all experimental measurements (200 out of 208 records). Trend and relevancy analyses indicated that the sensor sensitivity intensifies with increasing hydrogen concentration and decreasing temperature. The reduced graphene oxide (rGO) dose initially improves sensor sensitivity toward hydrogen detection up to a maximum value and then continuously decreases it. The analysis of variance approves that the ZnO-CoO sensor has the maximum value of least squares average = 42.3 for hydrogen detection over its experimental conditions. This study provides valuable insights for designing efficient ZnO-based sensors for hydrogen detection, ultimately contributing to safe hydrogen transportation/utilization.

中文翻译：

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开发堆叠机器学习模型来计算基于 ZnO 的传感器的氢检测能力

装饰有各种掺杂剂的氧化锌 (ZnO) 纳米复合传感器是检测低浓度氢 (H) 的常用工具。纳米复合材料的化学性质、温度和 H 浓度影响氢传感器的成功。为了调查这些变量对传感器性能的影响，进行了广泛的实验室规模研究，目前还没有模型将纳米复合材料的灵敏度与其影响变量联系起来。本研究提出了一种堆叠模型，通过集成 Extra 树和 XGBoost（极限梯度增强）回归器来精确地将 ZnO 基传感器的灵敏度与纳米复合材料的化学成分、H 浓度和温度联系起来。该模型的精度优于传统的人工神经网络，取得了出色的预测结果，平均绝对误差（MAE）= 0.11，均方误差（MSE）= 0.31，平均绝对百分比误差（MAPE）= 1.14%，R-平方 (R) = 0.9994 基于 208 个实际传感器灵敏度。此外，设计的堆叠模型预测了 206 个实验记录，相对误差范围为 -4% 到 8%。适用性域分析证实了几乎所有实验测量的有效性（208 条记录中的 200 条）。趋势和相关性分析表明，传感器灵敏度随着氢气浓度的增加和温度的降低而增强。还原的氧化石墨烯 (rGO) 剂量最初将传感器对氢检测的灵敏度提高到最大值，然后不断降低。方差分析证实 ZnO-CoO 传感器在其实验条件下检测氢的最小二乘平均值最大值 = 42.3。这项研究为设计用于氢气检测的高效氧化锌传感器提供了宝贵的见解，最终有助于安全的氢气运输/利用。