Dataset of design factors and corresponding properties for cellulose lightweight materials production

This dataset focuses on the design factors and corresponding properties for the production of cellulose lightweight materials (LMs). These materials, such as foams and aerogels, have distinctive mechanical and thermal properties, making them suitable for cushioning and insulative packaging. The dataset was developed to understand the various "design factors" and their sub-parameters that influence the properties of cellulose LMs, which are vital for packaging applications. Comprising 208 entries of air-dried LMs and 335 entries of freeze-dried LMs, it includes parameters such as the type and concentration of raw materials, method of porosity creation (freeze drying or air drying), drying conditions (temperature, time, surfactants, additives), and measured properties of the materials (apparent density, Young's modulus, yield stress, thermal conductivity). Data was extracted from peer-reviewed publications and patents, standardized for consistency, and processed with specific rules for material classification, concentration, and measurement units. The dataset can be used for machine learning to predict LM properties based on design factors, with a sample Python code provided for demonstration. Limitations include the underrepresentation of non-English publications and potential uncertainties in data estimated from images.

本数据集聚焦于纤维素轻质材料（cellulose lightweight materials, LMs）生产中的设计因素及其对应性能。这类材料如泡沫和气凝胶，具备独特的力学与热学性能，适用于缓冲与隔热包装场景。
本数据集旨在探究影响纤维素轻质材料性能的各类“设计因素”及其子参数，而这些性能对于包装应用而言至关重要。数据集共包含208条风干轻质材料样本与335条冻干轻质材料样本，涵盖的参数包括：原材料种类与浓度、孔隙制备方式（冻干或风干）、干燥条件（温度、时长、表面活性剂、添加剂），以及材料的实测性能（表观密度、杨氏模量、屈服应力、导热系数）。
所有数据提取自同行评议的期刊文献与专利，已进行标准化处理以保证一致性，并依据特定规则完成材料分类、浓度校准与测量单位统一。该数据集可用于机器学习任务，以基于设计因素预测轻质材料的性能，文中还附带了示例Python代码用于演示其应用。
本数据集存在一定局限：非英文文献的占比偏低，且通过图像估算得到的数据存在潜在不确定性。