Duluth prof invents fabric shredder to recycle clothing



Hands load fabric into a machine

Fast fashion produces clothes cheaply and quickly. Many of us have grown accustomed to clicking on an ad and getting a new top, dress or a pair of shorts on our doorstep just days or sometimes hours later, only to throw out the clothing after it quickly grows out of style.

But the pollution that clothing causes doesn't fade away like fashion trends.

Much of the clothing that’s tossed away ends up in landfills and produces methane, a potent greenhouse gas and a significant contributor to warming the climate. Decomposing clothes also can release PFAS, or forever chemicals, and microplastics.

And that’s where an innovative machine designed and built by an engineering professor and her students at the University of Minnesota Duluth could help, by recycling unwanted clothing instead of landfilling it.

Masked researchers load fabric into a machine
University of Minnesota Duluth associate professor Abbie Clarke-Sather (left) and junior Bruce Johnson (right) load fabric into a shredding machine in Duluth on May 20. The mechanism is designed to pull fabric apart, resulting in usable strands of material that can be re-woven into usable yarns and recycled.
Ben Hovland | MPR News

It's called the "fiber shredder." It's the brainchild of Abbie Clarke-Sather, associate professor in mechanical and industrial engineering at UMD, who’s been fine-tuning the contraption with her students for the past decade.

It's about the size of a copy machine. Inside, two drums rotate in opposite directions, with teeth on them “kind of like what you'd find on a fish hook,” she said during a recent demonstration.

Clarke-Sather dons a face mask, turns on a loud air filter and feeds strips of an old, holey, pink cashmere sweater into the top of the machine. Instead of chopping up the fabric, the drums pull apart the fibers. Ninety seconds later, she’s peering inside a bag full of pink thread that looks like cotton candy.

Fabric is torn apart in a machine
Fabric is shredded to its raw strands in a machine designed by University of Minnesota Duluth associate professor Abbie Clarke-Sather in Duluth on May 20.
Ben Hovland | MPR News

“Look at how long these threads are!” Clarke-Sather exclaims. “Two, three inches long? That's the thing that's really cool about this, is that it preserves the thread length," she explains.

Similar machines tear apart the fabric into smaller pieces or chunks. That material can be “downcycled,” repurposed into less valuable products ranging from insulation and carpet padding to stuffing for mattresses or dog beds. But it can't be spun back into yarn to make clothes again. That requires threads at least two inches long.

“Everybody's going for that holy grail of apparel-to-apparel recycling,” Clarke-Sather said. The fiber shredder, she said, makes that possible.

A woman holds strands of shredded fabric
Associate professor Abbie Clarke-Sather shows off strands of fabric, the results of her shredding machine, in her lab at the University of Minnesota Duluth on May 20.
Ben Hovland | MPR News

She’s close to sending the machine out for sale or rent to businesses seeking to recycle their textile waste. One of the places she plans to start is True North Goodwill in Duluth, which receives millions of pounds of donated clothes every year.

Goodwill sells about 60 percent of the donations they take in. The rest is compressed into thousand pound bales and stacked on the warehouse floor.

"These clothing items were either something that wasn't quality enough to sell — it had rips, tears and stains — or it was an item that just didn't sell,” said Scott Vezina, Vice President of Community Engagement at True North Goodwill.

Many of these leftover clothes will be shipped overseas. Some are repurposed into cleaning rags. But Vezina believes there are untapped markets for this vast amount of used textiles.

A man stands next to piles of used clothes in a warehouse
Scott Vezina, True North Goodwill’s vice president of community engagement, stands next to 1,000-pound clothing bales in the company’s Duluth warehouse on May 20.
Ben Hovland | MPR News

“Where the fiber shredder comes in is that it ends up repurposing the material in such an innovative way, that we're going to be able to discover more uses for it, more partners that want to be able to keep more of it out of landfills," Vezina said.

And the amount of textile waste that's landfilled is only increasing. The latest estimate from the U.S. Environmental Protection Agency found that about 17 million tons of textiles were landfilled in 2018, a 50 percent increase over the previous two decades.

One industry development experts say is largely to blame is the growth of "fast fashion," the mass production of cheap clothing that people tend to view as disposable.

"And so we have these increased rates of disposal that are piling up and creating more and more textile waste,” said Tasha Lewis, a professor in fashion and retail studies at the Ohio State University.

“We usually aren't getting rid of clothing because it's threadbare. It's usually because we don't like it, it's too old, we don't have a use for it,” Lewis said.

Used clothes sit in blue bins
Unsold used clothing is sorted in bins in True North Goodwill’s warehouse in Duluth on May 20.
Ben Hovland | MPR News

Only about 15 percent of textiles are recycled, according to the EPA. There isn’t an infrastructure in place in textile recycling similar to what exists for recycling aluminum cans or glass bottles, said Rachel Kibbe, CEO of American Circular Textiles, which advocates for greater clothing reuse and recycling.

There are large recycling plants just beginning to come online in the U.S. that use a chemical process to break down clothing into their basic building blocks. Mechanical recycling that doesn’t use chemicals is more challenging, Kibbe said, because of all the different fabric blends that make up clothing.

The “fiber shredder” struggles with some fabrics, mainly spandex and waterproof material, said Clarke-Sather. But the machine successfully pulls apart blended fabrics, including polyester blends, into usable threads.

It’s also small and portable. “I can literally just put this on a trailer and drive it to your retail store, and you can recycle what you need to,” she said. “Nobody's recycling at this tiny scale.”

Her students are working on a version that’s six times larger than her prototype that she hopes to station at Goodwill to help create a larger market for recycled fibers. But she acknowledges some kind of policy change is likely needed for that market to grow.

“There are some technologies, but there's not guaranteed buyers. So we need to actually incentivize people to put recycled fibers into their products,” said Clark-Sather.

Two people stand next to a large shredding machine
University of Minnesota Duluth associate professor Abbie Clarke-Sather (right) and junior Fox Zeppernick-Maki (left) show off their larger fabric shredding prototype in Duluth on May 20.
Ben Hovland | MPR News

Two years ago, California passed the nation’s first clothing recycling law that could help to jumpstart a market for recycled fabric.

It doesn’t require consumers to recycle clothing. Rather, clothing brands that want to sell their products in the state have to join a “producer responsibility organization” and pay a fee that funds the collection, reuse and recycling of textiles.

“By 2030, we're going to start collecting more clothing in the state of California than we have in the history of the planet,” said Kibbe. “The spirit of the bill is to recycle that which cannot be resold” and to keep clothing out of landfills.

Without such a law in place in Minnesota, Clarke-Sather is working hard to get the fiber shredder into the community. She’s lending it out to organizations that want to try it out, including a community art studio in Minneapolis. She even holds textile recycling office hours.

“I've talked to people that want to recycle their personal stashes all the way to a company across the ocean that wants to recycle their manufacturing waste,” she said. “I really want to encourage people to be creative.”



Source link

Leave a Reply

Subscribe to Our Newsletter

Get our latest articles delivered straight to your inbox. No spam, we promise.

Recent Reviews


Food delivery app development means engineering a three-sided platform connecting customers, restaurants, and drivers through a single real-time system. A production-ready MVP takes 4–6 months and costs $30,000–$120,000 depending on feature scope. The global market for online food delivery is projected to surpass $1.85 trillion by 2030 (Statista, 2025), making this one of the highest-ROI verticals in mobile commerce. This guide covers everything product and engineering teams need to build, launch, and scale a competitive food delivery platform in 2026.

Building a Food Delivery App in 2026? Start With a Free Architecture Review.

The Food Delivery Market in 2026: Size, Growth, and Opportunity

The global online food delivery market generated approximately $1.07 trillion in gross merchandise value in 2025 and is forecast to reach $1.85 trillion by 2030 at a CAGR of 10.4% (Statista, 2025). Online food orders have outpaced traditional dine-in by over 300% since 2014, a structural shift accelerated by COVID-19 that has since become permanent consumer behaviour.

Bloomberg Second Measure data from Q1 2026 shows DoorDash controlling approximately 67% of the US food delivery market by order volume. In India, Swiggy and Zomato dominate a market expected to reach $21 billion in GMV by 2026 (NRAI, 2025). The food industry contributes roughly 12% of India’s GDP and accounts for close to 40% of employment, underscoring the commercial weight behind digital food platforms.

Users aged 18–34 account for over 51% of all food delivery app orders globally (Statista, 2025). This mobile-first demographic makes native or cross-platform mobile performance a non-negotiable baseline for any new market entrant.

 Three Business Models for Food Delivery App Development

Food delivery app development supports three commercially proven business models. The choice made before development begins determines architecture, revenue structure, and the unit economics path. These models are not interchangeable mid-build.

  1. Aggregator Model: The app lists partner restaurants and routes orders to them; delivery is handled by each restaurant. Revenue comes from listing commissions, typically 15–30% per order. Lower technical complexity but limited margin control. Suitable as a starting point for regional platforms. Examples: early-stage Grubhub, regional Indian aggregators.
  2. Logistics Model (Order and Delivery): The platform manages both order routing and last-mile delivery using its own contracted driver network. Revenue comes from commissions plus delivery and service fees. This is the most technically complex model and the most defensible at scale because the platform controls the full customer experience. Examples: DoorDash, Uber Eats, Swiggy.
  3. Cloud Kitchen Model: The platform operates its own kitchen infrastructure under multiple virtual brand names from a single location with no physical storefront. Cloud kitchen revenue in India is projected to reach $2 billion in 2025 (NRAI, 2024). This model requires food operations expertise alongside the technology build.

Must-Have Features for Food Delivery App Development in 2026

A production-ready food delivery app development project requires features across three panels: the customer app, the restaurant dashboard, and the driver app. Missing a core feature in any single panel creates funnel friction that degrades order completion rates platform-wide, even if the other two panels are well-built.

Food delivery app development

Customer App

  • Restaurant discovery with advanced filters: cuisine type, dietary restrictions, delivery time, estimated cost, and distance
  • Real-time GPS order tracking with dynamically updated ETA calculations, accurate to within 2 minutes
  • In-app payment supporting cards, UPI, mobile wallets, and BNPL options with PCI DSS compliance
  • AI-powered recommendations surfacing reorders, personalised dish suggestions, and time-aware menus (powered by AI and ML development)
  • Push notifications for order status milestones, promotions, and re-engagement campaigns
  • Ratings and reviews with photo upload support and restaurant response capability

 Restaurant Dashboard

  • Live order management queue with accept, reject, and item-level modification controls visible in under 3 seconds
  • Menu management: item-level pricing, availability toggles, image uploads, and category organisation
  • Performance analytics covering order volume, peak hours, cancellation rate, average order value, and revenue trends
  • Automated out-of-stock updates that propagate to customer-facing menus in real time, preventing failed orders
  • Promotional tools including discount codes, bundle offers, and sponsored placement; designed for high conversion by Ailoitte’s UI/UX design practice

Driver App

  • Automated order dispatch with AI-based route optimisation via Google Maps Platform Directions API or Mapbox
  • In-app navigation with live traffic rerouting and turn-by-turn directions including last-metre guidance
  • Earnings dashboard with real-time totals, per-trip breakdown, incentive progress, and payout history
  • Masked customer contact numbers for privacy-compliant in-app calling without number exposure
  • Delivery proof capture via photo and optional e-signature to reduce refund disputes

Recommended Technology Stack for Food Delivery App Development

The recommended stack for food delivery app development is React Native or Flutter for mobile, Node.js (NestJS) for the API layer, PostgreSQL for transactional data, Redis for real-time caching, and Google Maps Platform for routing. These choices determine how well the platform handles peak-hour concurrency, how quickly it ships new features, and what it costs to operate at scale.

Mobile Frontend

React Native or Flutter deliver near-native performance from a shared iOS/Android codebase. React Native is preferred for teams with deep JavaScript experience; Flutter is preferred where pixel-perfect UI fidelity matters most. According to Google I/O 2025, Flutter adoption in on-demand and food delivery apps grew significantly in 2025, driven by superior animation performance on lower-end Android devices.

Backend API Layer

Node.js (Express or NestJS) handles the primary API layer with its event-driven, non-blocking I/O architecture, well-suited for concurrent real-time order events. Python (FastAPI or Django) is deployed for ML-based services including recommendation engines and demand forecasting. PostgreSQL manages transactional order data; Redis handles session management, real-time caching, and queue processing.

Real-Time Communication

WebSockets via Socket.io propagate live order status across all three app panels. Firebase Realtime Database is a suitable managed alternative for teams at earlier infrastructure maturity stages. Sub-second latency on status updates is a baseline user expectation in 2026.

Cloud Infrastructure

AWS (ECS or EKS), Google Cloud Platform, or Azure for hosting. Docker and Kubernetes handle containerisation and auto-scaling during peak demand windows. A CDN such as AWS CloudFront or Cloudflare serves menu images and static assets, targeting sub-100ms response times globally.

Key Third-Party Integrations

  • Google Maps Platform: Directions API, Distance Matrix API, and Places API for routing and location search
  • Firebase Cloud Messaging (FCM): unified push notification delivery for iOS and Android
  • Payments: Stripe (global), Razorpay (India), or PayPal, all PCI DSS compliant
  • Analytics: Mixpanel or Amplitude for behavioural product analytics; Firebase Crashlytics for crash monitoring

Food Delivery App Development Cost and Timeline

Food delivery app development costs range from $30,000 to $250,000 or more, depending on platform scope, number of markets, compliance requirements, and whether the build includes a cloud kitchen management layer. The table below shows Ailoitte’s three standard scoping tiers based on engagements completed between 2023 and 2026.

Tier Scope Cost Range Timeline
MVP (Startup scale) Customer + Driver apps, basic restaurant panel $30,000–$60,000 16–20 weeks
Full Platform v1 All three panels, real-time tracking, payments $60,000–$120,000 24–32 weeks
Enterprise (Enterprise build) Multi-city, AI recommendations, analytics dashboard $120,000–$250,000+ 9–18 months

Note: All figures are estimates from Ailoitte’s internal project data (2023–2026). Actual costs vary by team location, feature complexity, and compliance requirements. [Estimate based on Ailoitte internal project data, 2023–2026]

The single largest cost driver in food delivery app development is the real-time system architecture. Supporting live GPS tracking, dynamic ETAs, concurrent driver assignment, and sub-second push notification delivery at scale requires careful upfront architectural investment. Teams that underinvest here at the MVP stage routinely face expensive re-architecture within 12–18 months of launch.

 Get a Precise Cost Breakdown for Your Food Delivery App

The table above is a starting point. Share your feature wishlist and target market and Ailoitte will return a scoped estimate with a fixed-price delivery option within 48 hours. No obligation.

►  Request Your Custom Estimate  →  ailoitte.com/food-delivery-app-development

What Changed in 2026: Key Shifts for Food Delivery App Development

The three most important changes affecting food delivery app development in 2025–2026 are: AI personalisation becoming a baseline expectation, delivery windows compressing to under 20 minutes in Tier 1 markets, and sustainable packaging compliance entering regulatory scope in EU jurisdictions. Any product team starting a build today must account for all three.

AI powered Food Delivery app

AI-Powered Personalisation Is Now a Baseline Expectation

Platforms without recommendation engines are losing retention to those that surface personalised reorders, dietary-based suggestions, and time-aware menus. Major platforms attribute a significant share of order volume to AI-driven surfacing [Estimate based on industry observation, no primary source available]. Ailoitte’s AI development practice recommends building a lightweight ML recommendation layer from the first sprint rather than retrofitting it post-launch, when training data has accumulated without the correct logging infrastructure in place.

Delivery Windows Have Compressed to Under 20 Minutes in Tier 1 Markets

The standard delivery SLA in major metros has fallen from 45 minutes to under 20 minutes in several food categories, driven by quick-commerce entrants like Blinkit and Zepto entering the food segment. This demands tighter driver dispatch algorithms, predictive stocking for cloud kitchens, and backend infrastructure capable of sub-second latency on driver assignment calls. Any food delivery app development targeting Tier 1 Indian or European cities must account for this in the initial architecture brief.

Sustainable Packaging Compliance Is Entering Regulatory Scope

Several EU member states are mandating that food delivery platforms offer plastic-free packaging options and disclose per-order packaging material data to consumers (EU Single-Use Plastics Directive 2019/904). Platforms targeting European markets in 2025–2026 need to include packaging metadata fields in the restaurant menu schema from day one, not as a future addition. 

In our food delivery app development engagements, the two components teams most consistently underestimate are the restaurant-side order management interface and the driver dispatch logic. A poorly designed restaurant panel produces elevated cancellation rates, a problem that damages customer retention before it becomes visible in top-line analytics.  

We now recommend that any client building a logistics-model platform allocate at minimum 30% of the front-end development budget to the restaurant and driver panels, not solely to the customer app.

FAQs

How long does food delivery app development take?

A food delivery app MVP takes 16–24 weeks from kickoff to launch: 2–3 weeks for discovery and architecture, 10–14 weeks for core development, and 4–6 weeks for QA, performance testing, and app store submission. A full three-panel platform with AI personalisation takes 6–9 months. See Ailoitte’s on-demand app development page for typical sprint breakdowns.

 

How much does it cost to build a food delivery app?

Food delivery app development costs $30,000–$60,000 for a single-market MVP, $60,000–$120,000 for a full three-panel platform with real-time tracking, and $120,000–$250,000 or more for a multi-city enterprise build with AI personalisation. The most significant cost drivers are real-time architecture complexity, Google Maps Platform API usage at scale, and driver dispatch algorithm sophistication.

What is the best technology stack for a food delivery app?

React Native or Flutter for mobile, Node.js (NestJS) for the API layer, PostgreSQL for transactional data, Redis for real-time caching, and Google Maps Platform for routing. This combination covers the full feature surface of a production food delivery app and benefits from the largest available engineering talent pool for ongoing hiring.

Can I build a food delivery app without a driver network?

Yes. The aggregator model allows restaurants to manage their own delivery, eliminating the need for a driver app and dispatch system. This is a common starting point for regional platforms. The trade-off is lower per-order margin and dependence on restaurant-side delivery capacity. See Ailoitte’s on-demand app development solutions for aggregator-specific architecture patterns.

 

What differentiates winning food delivery apps in 2026?

Speed, personalisation, and reliability. Users in competitive markets expect sub-30-minute delivery with live tracking and AI-driven recommendations. Platforms that hit delivery SLAs consistently outperform on long-term retention regardless of promotional discounting. The infrastructure to deliver this reliably, including routing algorithms, driver incentive design, and kitchen communication tooling, is where food delivery app development investment pays the highest long-term dividend.

Discover how Ailoitte AI keeps you ahead of risk

Sunil Kumar

Sunil Kumar is CEO of Ailoitte, an AI-native engineering company building intelligent applications for startups and enterprises. He created the AI Velocity Pods model, delivering production-ready AI products 5× faster than traditional teams. Sunil writes about agentic AI, GenAI strategy, and outcome-based engineering. Connect on

LinkedIn



Source link