Scribbler vs Typeset

Explore the showdown between Scribbler vs Typeset and find out which AI Summarizer tool wins. We analyze upvotes, features, reviews, pricing, alternatives, and more.

When comparing Scribbler and Typeset, which one rises above the other?

When we contrast Scribbler with Typeset, both of which are exceptional AI-operated summarizer tools, and place them side by side, we can spot several crucial similarities and divergences. The community has spoken, Typeset leads with more upvotes. Typeset has 24 upvotes, and Scribbler has 8 upvotes.

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Scribbler

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What is Scribbler?

Scribbler is a ai-powered tool that provides instant insights and summaries for your favorite podcasts and youtube videos. It includes an intuitive search tool, a vast podcast library, subscriptions for your favorite podcasts and more. Whether you're a casual listener or an avid fan, Scribbler enhances your digital experience by making it easy to consume the content you love in a timely manner.

Typeset

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What is Typeset?

Your platform to explore and explain papers. Search for 270M+ papers, understand them in simple language, and find connected papers, authors, topics.

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Scribbler Upvotes

Typeset Upvotes

24🏆

Scribbler Top Features

Summarize and get key insights from Youtube Videos and Podcasts
Follow your favorite podcasts and get weekly updates,

Typeset Top Features

No top features listed

Scribbler Category

Summarizer

Typeset Category

Summarizer

Scribbler Pricing Type

Freemium

Typeset Pricing Type

Free

Scribbler Technologies Used

Next.js

Vercel

Stripe

React

Google Tag Manager

Tailwind CSS

Typeset Technologies Used

Amazon Web Services

jQuery

Bootstrap

Scribbler Tags

Podcasts

YouTube

Summary Generator

YouTube summarizer

Podcast summarizer

Key insights

Typeset Tags

Content Summary

AI Whitepapers

AI Emails

Scribbler Average Rating

No rating available

Typeset Average Rating

4.00

Scribbler Reviews

No reviews available

Typeset Reviews

Sara Sara

The simulation model validated experimental J-V and external quantum efficiency (EQE) to demonstrate an improvement in perovskite (PSK) solar cell (PSC) efficiency. The effect of interface properties at the electron transport layer (ETL)/PSK and PSK/hole transport layer (HTL) was investigated using the Solar Cell Capacitance Simulator (SCAPS). The interfaces between ETL, PSK, and HTL were identified as critical factors in determining high open-circuit voltage (Voc) and FF. In this study, the impact of two types of interfaces, ETL/PSK and PSK/HTL, were investigated. Lowering the defect density at both interfaces to 102 cm−2 reduced interface recombination and increased Voc and FF.The absorber layer defect density and n/i interface of perovskite solar cells were investigated using the Solar Cell Capacitance Simulator-1D (SCAPS-1D) at various cell thicknesses. The planar p-i-n structure was defined as PEDOT:PSS/Perovskite/CdS, and its performance was calculated. With a defect density of <1014 cm−3 and an absorber layer thickness of >400 nm, power conversion efficiency can exceed 25%. The study assumed a 0.6 eV Gaussian defect energy level beneath the perovskite's conduction band, which has a characteristic energy of 0.1 eV. These conditions produced the same result on the n/i interface. These findings place constraints on numerical simulations of the correlation between defect mechanism and performance

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