Environmental Function Evaluation and Preparation of Green Decorative Materials in Indoor Design

As a result of social-economic integration, personalized interior design not only brings comfort and beauty, but also seriously pollutes the indoor environment. In future, consumers will pursue greener, more environmentally-friendly, and more efficient decorative materials for indoor design [1-4]. The greening of indoor decorations helps to improve the living environment and the health condition [5-7]. Visually speaking, green indoor decorative materials can improve the mood of the occupants, and even relieve fatigue [8-15]. With the growing awareness of health, there is a rising demand for green decorative materials in furniture and indoor design [16-19]. In recent years, various green decorative materials with diverse functions have been researched and developed. This trend testifies the surge in the demand for indoor green decorative materials.

Ling [20] noted that the functions and environmental friendliness of indoor decorative nanomaterials are the current research hotspot, carried out three survey experiments on the role of decorative nanomaterials in indoor design and the application of environmental protection concepts, and compared the environmental parameters of traditional decorative materials and those of green nanomaterials. The experimental results show that indoor decorative design with green nanomaterials reduces the emissions of harmful and toxic materials. Taking a construction project as the object, Lu [21] monitored the gaseous pollution (e.g., hydrogen sulfide and sulfur dioxide), solid pollution, water pollution, light pollution and noise pollution sources in the construction site, and demonstrated that high-quality construction of green buildings can be realized through overall design of energy conservation and environmental protection, the structural optimization design, and heating, ventilation, and air conditioning (HVAC) design of green buildings. Wang and Li [22] combined high-performance phase change composites with the system of energy-saving green indoor decorative materials into a temperature-automatically controlled energy-efficient green decorative material system based on phase change composites. Ryder [23] pointed out the development trend of modern building materials (traditional building materials cannot meet people’s needs, while new environmentally friendly building materials can satisfy our demand), and detailed the new green construction materials and their applications. Li [24] described a series of commercial processes for indoor design coatings, which are pollution-free, low-cost, fast to apply, and user friendly, with a small capital/equipment cost.

The existing studies lack the overall evaluation of environmental functions for green decorative materials in indoor design. This paper probes deep into the functions, roles, and preparation of green decorative materials from the following aspects: the development of green decorative materials, the different types of green decorative materials, the environmental functions of green decorative materials in indoor space, and the limitations and prospects of the application of green decorative materials. Starting from the current state of household-based indoor green decorative materials in modern cities, this paper mainly involves the following contents: Section 2 expounds the types and purchase principles of green decorative materials, and specifies the preparation flow of wood product coating for green decorative materials in indoor design. Section 3 establishes an evaluation model for the environmental functions of green decorative materials, constructs the stochastic dominance matrix for the indices, and computes the order value of each index. Section 4 adopts fuzzy Dempster–Shafer (D-S) evidence theory to develop a prediction algorithm for the environmental function of indoor decorations, and thus realize the evaluation of the greenness of decorative materials in indoor design. Experimental results demonstrate the effectiveness of our algorithm, and the good performance of the prepared super-hydrophobic and oleophobic coatings.

3.1 Stochastic dominance matrix

Let R_i be type i green decorative material. Then, the set of u types of green decorative materials can be denoted as R={R₁,R₂,...,R_i,...,R_n}. Let FZ_j be an evaluation index of the environmental functions of green decorative materials. Then, the set of m such indices can be denoted as FZ={FZ₁,FZ₂,...,FZ_i,...,FZ_m}. Based on the calculation of greenness, the environmental function levels of an index FZ_j can be determined as ψ^τ={ψ¹=1, ψ²=2, ψ³=3, ψ⁴=4,ψ⁵=5}.

The stochastic dominance between the indices FZ₁, FZ₂, ..., FZ_l, ..., FZ_m can be determined as follows: Firstly, the cumulative probability distribution functions (CPDFs) of all random variables, which bear on the values of the evaluation indices, are compared randomly by the optimal allocation principle of stochastic dominance. Then, the all indices can be ranked by superiority/inferiority.

To begin with, it is necessary to compute the expectation p_S of the CPDF G_S(φ) of the environmental function levels corresponding to the greenness scores of green decorative material R_i on all indices. Contraposing expert comment set D, the number of expert comments on green decorative material R_i, which assign the environmental function on index FZ_j to level ψ^τ, is denoted as CO^τ_ij. According to CO_ij and CO^τ_ij, the probability for the environmental function of green decorative material R_i on index FZ_j to fall on level ψ^τ can be calculated by:

$\overset{\tau }{\mathop{pr{{o}_{ij}}}}\,=\frac{\overset{\tau }{\mathop{C{{O}_{ij}}}}\,}{C{{O}_{ij}}}$       (1)

Let G_ij(φ) be the CPDF of environmental function of green decorative material R_i on index FZ_j; G_if(φ) be the CPDF of the environmental function of green decorative material R_i on index FZ_f falling on level ψ^τ. When G_ij(φ) has stochastic dominance over G_if(φ), FZ_j must have stochastic dominance over FZ_f. The CPDF of the environmental function of green decorative material R_i on index FZ_j falling on level ψ^τ can be given by:

$G_{i j}(\phi)=\sum_{\phi \geq \psi^{\tau}} \operatorname{Pro}_{i j}^{\tau}$       (2)

On this basis, it is possible to construct the expected vector P_i=(p_ij)_i*1 for the environmental function level of green decorative material R_i on index FZ_j, where p_ij can be defined as:

${{p}_{ij}}\left( \phi  \right)=\sum\limits_{\tau =1}^{5}{\underset{ij}{\mathop{Pro\text{  }{{\psi }^{\tau }}}}\,}$       (3)

Next, it is necessary to construct the stochastic dominance matrix sⁱ_jf between any two indices of green decorative material R_i. The comments of all experts are converted into the probability distributions of the environmental function levels of the green decorative material on the indices. After that, any two indices of green decorative material R_i are compared by the stochastic dominance rule. The stochastic dominance S_i=(sⁱ_jf)_Mzt×Mst between indices FZ_j and FZ_f of green decorative material R_i can be characterized by:

$s_{j f}^{i}=\left\{\begin{array}{l}F O R, G_{i j}\left(\psi^{\tau}\right) F O R G_{i f}\left(\psi^{\tau}\right) \\ \operatorname{SOR}, G_{i j}\left(\psi^{\tau}\right) \operatorname{SOR} G_{i f}\left(\psi^{\tau}\right) \\ \operatorname{TOR}, G_{i j}\left(\psi^{\tau}\right) \operatorname{TOR} G_{i f}\left(\psi^{\tau}\right) \\ /, \text { Otherwise }\end{array}\right.$       (4)

where, sⁱ_jf=FOR means index FZ_j of green decorative material R_i has first-order stochastic dominance over FZ_f; sⁱ_jf=SOR means index FZ_j of green decorative material R_i has second- order stochastic dominance over FZ_f; sⁱ_jf=TOR means index FZ_j of green decorative material R_i has third- order stochastic dominance over FZ_f; sⁱ_jf=/ means stochastic dominance does not exist between indices FZ_j and FZ_f.

3.2 Oder value of indices

(1) Matrix of stochastic dominance degrees

Based on the constructed stochastic dominance matrix S_i=(s_jf)_Mzt×Mst, an order function is established by hesitant fuzzy method to further depict the stochastic dominance degree between any two indices FZ_j and FZ_f of green decorative material R_i. Then, the overall order matrix is set up through pairwise comparison of indices to compute the order value of each index. On this basis, the indices of the green decorative material can be ranked. Let ɑ_iβ_i be the preference threshold of green decorative material R_i. Then, the matrix of stochastic dominance degrees RD_i=[rd_i(FZ_j,FZ_f)]_M×N can be derived by:

$r d_{i}\left(F Z_{j}, F Z_{f}\right)=\left\{\begin{array}{l}1, r_{i h}^{j}=S D, \text { and } p_{i j} \geq p_{i f}+\beta_{i} \\ \frac{p_{i j}-p_{i f}}{\beta_{i}}, s_{i f}^{j}=S D, \text { and } p_{i f}<p_{i j}<p_{i j}+\beta_{i} \\ 0, \text { Otherwise }\end{array}\right.$        (5)

β_i can be calculated by:

$\beta_{i}=\frac{2}{m(m-1)} \sum_{j=1}^{m} \sum_{f=1 f \neq j}^{m} \xi_{j f}^{i}(m=1,2, \ldots, 21 ; f=1,2, . ., 21)$

where, $\xi_{j f}^{i}=\left\{\begin{array}{l}p_{i j}-p_{i f}, p_{i j} \geq p_{i f} \\ 0, p_{i j}<p_{i f}\end{array}\right.$       (6)

For RD_i=[rd_i(FZ_j,FZ_f)]_m×m, the dominance of FZ_j over FZ_f is positively correlated with the value of rd_i(FZ_j,FZ_f).

(2) Order value of each index

To compute the order value δ_i(FZ_j) for each index of each green decorative material, the first step is to calculate the outflow δ⁺_i(FZ_j) and inflow δ^-_i(FZ_j) of index FZ_l of green decorative material R_i, based on the stochastic dominance matrix RD_i=[rd_i(FZ_j,FZ_f)]_m×m. Note that δ⁺_i(FZ_j) and δ^-_i(FZ_j) are the confidence of FZ_j being superior and inferior to the other index, respectively. The greater the δ⁺_i(FZ_j), the smaller the δ^-_i(FZ_j), and the better the environmental functions of the material on the corresponding index. According to the matrix of stochastic dominance degree RD_i=[rd_i(FZ_j,FZ_f)]_m×m, δ⁺_i(FZ_j) and δ^-_i(FZ_j) can be respectively defined by:

$\delta _{i}^{+}\left( F{{Z}_{j}} \right)=\sum\limits_{m}{rd}\left( F{{Z}_{j}},F{{Z}_{f}} \right)$        (7)

$\delta _{i}^{-}\left( F{{Z}_{j}} \right)=\sum\limits_{m}{rd}\left( F{{Z}_{f}},F{{Z}_{j}} \right)$       (8)

According to δ⁺_i(FZ_j) and δ^-_i(FZ_j), the order value δ_i(FZ_j) of each index of each green decorative material can be calculated by:

${{\delta }_{i}}\left( F{{Z}_{j}} \right)=\delta _{i}^{+}\left( F{{Z}_{j}} \right)-\delta _{i}^{-}\left( F{{Z}_{j}} \right)$       (9)

The greater the order value δ_i(FZ_j), the better the environmental functions of the material on index FZ_j.

Table 1 lists the outflow, inflow, and order value of the environmental functions of green decorative materials. The order value δ is positively correlated with the environmental function level of the corresponding green decorative material on index FZ_j. Thus, the evaluation indices can be ranked by the order value.

Table 1. Outflow, inflow, and order value of each index

Table 2. Fused environmental function information of different green decorative materials

Table 3. Coating transmittance at different first pulling rates

Table 2 shows the fused environmental function information of different green decorative materials. Materials 1 and 2 were unqualified in terms of production process, and qualified on the other aspects. Before the experiments, an expert survey was carried out. The results indicate that the environmental functions of both materials are unqualified. Thus, the results of our algorithm are inconsistent with the results of expert survey. The proposed evaluation model achieved an accuracy of 97% in the environmental function evaluation of green decorative materials. This is of certain practical significance, as it meets the application standard for household-based indoor design.

The high performance of the prepared super-hydrophobic and oleophobic coating was verified through contrastive experiments. Tables 3 and 4 present the coating transmittance at different first and second pulling rates. The pulling rate was set to 1.0cm/s, 2.0cm/s, 3.0cm/s, 4.0cm/s, and 5.0cm/s, in turn. As shown in Table 3, the transmittance of the flat coating gradually decreased with the growth of first pulling rate. When the first pulling rate was below 2.0cm/s, the mean transmittance of the prepared coating was above 0.8 in the range of visible light wavelengths; When the first pulling rate was above 2.0cm/s, the mean transmittance of the prepared coating was smaller than 0.8 in the range of visible light wavelengths. Hence, the transmittance of the super-hydrophobic and oleophobic coating is superior at a low first pulling rate.

As shown in Table 4, within the range of visible light wavelengths, the coating transmittance did not fluctuate significantly, as the second pulling rate changed from 1.0cm/s, 2.0cm/s, 3.0cm/s, 4.0cm/s, to 5.0cm/s. In the short wavelength range, the transmittance quickly declined until 0. Besides, the mean transmittance did not drop significantly, with the growth of second pulling rate, and the standard deviation remained small. Therefore, the transmittance of the prepared super-hydrophobic and oleophobic coating is not greatly affected by second pulling rate. Figure 3 shows the composite performance of the prepared coating. Obviously, our coating achieved the best overall performance at the second pulling rate of 1.0cm/s.

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Figure 3. Composite performance of the prepared coating

The super-hydrophobic and oleophobic coating was prepared with nontoxic or low-toxic solvents. Table 5 shows the physical properties of the coating prepared from aminopropylene resin. Before second pulling, the suspension was prepared with four solvents: isopropanol, n-ethane, acetone, and valinone. The coating prepared with the first three solvents were not super-hydrophobic, while that prepared with isopropanol and acetone was both hydrophobic and oleophobic; that prepared with valinone was the only coating realizing super-hydrophobicity and oleophobicity, but the transmittance of the coating was merely 80.59%. Table 6 shows the physical properties of the coating prepared from polysiloxane resin. Before second pulling, the suspension was prepared with four solvents: isopropanol, n-ethane, acetone, and valinone. The coatings prepared with all four solvents achieved super-hydrophobicity and oleophobicity. The transmittance of the coating prepared with acetone was only 67.9%, while that of the coating prepared with any of the other solvents surpassed 85%.

Table 4. Coating transmittance at different second pulling rates

Table 5. Physical properties of the coating prepared from aminopropylene resin

Table 6. Physical properties of the coating prepared from polysiloxane resin

4.1.png

(1)

4.2.png

(2)

Figure 4. Wear resistance of the prepared coating

Figure 4 shows the test results on the wear resistance of the coatings prepared with aminopropylene resin and polysiloxane resin. The wear resistance test was carried out in the following steps: Place the super-hydrophobic and oleophobic coating sample with an additional 80g weight evenly on a piece of sandpaper, and make the sample move forward at uniform speed by 15cm against the friction of the sandpaper. Four flips of the sample were treated as a cycle. Figure 4 shows the change of water contact angle of the sample after each cycle. It can be seen that the water contact angle of the two coatings both reduced with the growing number of cycles. After 8 cycles, the water contact angle of the coating prepared with polysiloxane resin gradually stabilized, while that of the coating prepared with aminopropylene resin fluctuated. This means the coating prepared with polysiloxane resin is relatively stable and good in performance.