AFQH 3D Wall Edging Self Adhesive, Pattern Molding Peel and Stick Wallpaper Border, Flexible Wall Base Moulding Trim Mirror Border Wall Lines for Home DIY Decoration Treatment

Features：

* brand new and high quality.

*High quality material: It is made from soft PE foam, eco-friendly, nontoxic, anti-corrosion, flame resistant, waterproof, oilproof and durable.

*Self-adhesive: Soft and elastic, strong adhesion, peel and stick, easy to install and remove. You just peel off and stick, no drilling needed, no residues and damage.

*3D embossing: The three-dimensional embossing process creates a visually tactile texture , great decor for your room.

*Wide application: Suitable for background wall, wall fence, skirting, balcony decoration, background frame, door frame sill decoration, wall repair, picture frame and more.

*Multifunction: Decorative moulding trims strips for edge, corner and wall. Can be used to make some DIY decoration to your home

*Cut easily: Can be cut with scissors to different sizes and lengths to meet your needs.

*Easy to clean: It is oilproof and waterproof, simply wipe with cloth.

*Suitable surfaces: Can be installed on the smooth and flat surfaces, such as, tiles, wood, glass, plastic, metal and more.

*Practical: Waterproof, anti-compression and anti-collision, easy to use with adhesive, easy to beautify the wall, wall repair and more.

*Easy to install: Comes with adhesive backing tape, you just clean the surface, peel and stick to the surface, no other tools required, no damage to wall surfaces.

Warm tips:

Please clean the surface before installation, or it will fall off.

It is not recommended to rough surfaces.

Specification:

Material: PVC/XPE foam/PE foam

Color:As shown

Size :

Length: 2.3m

Width: 8cm

Thickness: 4.5mm

Package Contents:

1 x adhesive foam molding trim

Note:

1. The real color of the item may be slightly different from the pictures shown on website caused by many factors such as brightness of your monitor and light brightness.

2. Please allow slight manual measurement deviation for the data.