Badger Spf 30 Face Mineral Sunscreen Cream
Product Images NDC 62206-3063

Product Visual Gallery

This gallery contains 2 technical images submitted to the FDA as part of the official labeling for Badger Spf 30 Face Mineral Sunscreen (NDC 62206-3063). Unlike standard consumer photos, these assets often include clinical data figures, molecular chemical structures, and official manufacturer packaging layouts.

As provided by W.s. Badger Company, Inc., these visuals offer a comprehensive scientific overview of the product's physical and chemical identity, aiding pharmacists and researchers in product verification and study.

FDA Label Image

Spf 30 Face (box)

This is a drug facts label for a mineral sunscreen from W.S. Badger Company, Inc. The product contains 80% organic ingredients, is water-resistant, and offers broad-spectrum SPF 30 protection with blue light defense. It is designed to prevent sunburn and decrease the risk of skin cancer and premature skin aging. The sunscreen is lightweight, waterless, and suitable for all skin tones, incorporating golden seaweed extract for added skin nourishment. The formula includes sheer zinc oxide for natural, matte coverage and contains seven clean ingredients, making it dermatologist-tested and effective at moisturizing the skin.*

FDA Label Image

Spf 30 Face Tube Label (Spf 30 Face Label)

This is a description of a mineral sunscreen by Badger, featuring a Weightless Moisturizer with Sheer-Matte Finish containing 80% organic ingredients. The sunscreen offers Broad Spectrum SPF 30 and is water-resistant for 40 minutes with added Blue Light Defense. It contains Golden Seaweed Extract for soothing and nourishing the skin, along with sheer zinc oxide for a natural matte finish. The product is designed for all skin tones and is made with clean, dermatologist-tested ingredients. The brand emphasizes nature's protection for healthy skin. It is recommended to apply liberally 15 minutes before sun exposure and reapply every 2 hours.*

* These product label images have been analyzed using experimental machine learning. Please verify findings with the primary label text.