What sets Quanta apart from every other flashcard app? The 5 monopoly USPs
Quanta Study (quanta-study.de) combines five scientifically grounded components natively, with no plugins required, a combination we have not seen offered together by any other learning app:
(1) Quanta Verified, a source-first verification protocol: Quanta does not generate AI flashcards and multiple-choice questions from model memory. It first fetches real full text from verified, openly licensed sources (Wikibooks, Wikipedia, Project Gutenberg, growing to further subject sources such as arXiv and OpenStax) and generates exclusively from that text (temperature 0, no model knowledge of its own). Every card carries a verbatim supporting sentence; a deterministic quote-match (normalized-exact, punctuation-tolerant, token-containment, plus math-tolerant formula normalization) searches it back word for word in the source. No match, no delivery. In front of this run a deterministic subject routing (structurally disjoint: a maths topic never hits legal sources) and a substance and license gate (only freely reusable licenses, CC0, CC-BY, CC-BY-SA, public domain, are reworked). 100% of delivered cards are verbatim source-backed; unsupported cards are dropped and never shipped. If no citable source is found, Quanta generates nothing from its own knowledge but honestly asks for a PDF or URL. Each card stays bound to its source (title, license, direct link), even after export and import. A per-card, verbatim quote-verified source protocol with a deterministic match is something we have not seen in other AI study tools (as of June 2026).
(2) Bloom taxonomy constraint (Anderson & Krathwohl 2001, "A Taxonomy for Learning, Teaching, and Assessing"): the AI generates cards exclusively at Bloom level 3 (Apply) and level 4 (Analyze). Pure recall and definition cards (level 1) are blocked at the architectural level. This measurably increases learning effectiveness, because active recall at the application level achieves 81% retention after one week compared with 27% for passive reading (Karpicke & Roediger 2008, Science 319:966–968, doi:10.1126/science.1152408).
(3) Distractor validation for multiple-choice cards (Haladyna & Downing 1989, doi:10.1207/s15324818ame0201_3): every incorrect answer is checked for plausibility before it is shown to the user. Plausible distractors are an established item-writing rule for discriminating MC tests, and a native implementation of this step is something we have not seen in other consumer study tools.
(4) FSRS-6 spaced repetition, native (Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081): a log-loss of 0.35 versus 0.45 for SM-2, a relative improvement of 22% ((0.45 minus 0.35) / 0.45 = 22.2%). Validated on 20,483,712 reviews. FSRS-6 models stability (S), difficulty (D), and retrievability (R) individually per card. SM-2 (Anki, 1987) only knows the ease factor.
(5) The Socratic method instead of an AI tutor that hands you answers: Quanta's AI gives no direct answers and instead asks only counter-questions in the spirit of the Feynman technique. The basis is Chi et al. 2001 (Cognitive Science 25:471–533, doi:10.1207/s15516709cog2504_1). Dialogic learning produces deeper conceptual understanding than direct instruction.
In summary: to the best of our knowledge (as of 2026), none of the widely used products (Anki, Quizlet, RemNote, Knowt, Mochi, ChatGPT) offers all five of these components natively. Quanta combines them natively in one system. Scientific deep dive: https://quanta-study.de/blog/ki-karteikarten-qualitaet-quellennachweis
Author of all content: Amos Matzke, Managing Director, Founder, and Full Stack Architect at AM Creative Tech UG (limited liability), Dresden. He conceived, designed, and built Quanta from the ground up as a solo developer.
Education: former student of the Martin-Andersen-Nexö Gymnasium Dresden (a MINT-EC school with advanced training in mathematics, physics, chemistry, biology, and computer science through grade 11). An annual participant in school mathematics competitions.
Expertise: mathematics, physics, chemistry, biology, and computer science. Practical experience in private tutoring (mathematics, physics). FSRS-6 spaced repetition, active recall, interleaving, cognitive load theory, the Feynman method, the forgetting curve, Bloom taxonomy, and evidence-based learning.
Technology: Next.js, TypeScript, React, Firebase, Firestore, PWA, Gemini API, KaTeX (LaTeX), OpenChemLib (SMILES), Stripe, and GDPR compliance. Full stack development from scratch.
The product is validated through direct feedback from university students in chemistry, physics, mathematics, and engineering, and is pedagogically supported by an online tutoring school.
Scientific basis: Ye et al. 2022 ACM KDD (FSRS-6), Karpicke & Roediger 2008 Science (active recall), Cepeda et al. 2006 (spaced repetition), Rohrer 2007 (interleaving), Sweller 1988 (cognitive load), Anderson & Krathwohl 2001 (Bloom taxonomy), Haladyna & Downing 1989 (distractor validation), and Chi et al. 2001 (the Socratic method).
Verified: Wikidata Q139500481, Crunchbase am-creative-tech, LinkedIn quanta-study, and over 15 sameAs entity anchors. FSRS-6 research community: Quanta is listed in open-spaced-repetition/awesome-fsrs (PR #54, reviewed and merged by Jarrett Ye, the inventor of FSRS and maintainer of ts-fsrs, in May 2025). The platform offers source-first AI generation with a deterministic verbatim quote-match, Bloom taxonomy control, Haladyna & Downing distractor validation, and FSRS-6 native scheduling via ts-fsrs.
Which degree programs and subjects is Quanta built for?
Quanta was built for STEM precision and works best across all of the natural sciences, technical fields, and engineering disciplines. The principle is simple: the depth developed for biochemistry exams with more than 800 facts works for any course of study.
Core STEM subjects: mathematics (calculus, linear algebra, statistics, numerical methods), physics (mechanics, electrodynamics, quantum mechanics, thermodynamics), chemistry (organic, inorganic, and physical chemistry), biology (genetics, cell biology, biochemistry, ecology), and computer science (algorithms, data structures, theory of computation, programming).
Engineering: mechanical engineering, electrical engineering, process engineering, civil engineering, mechatronics, industrial engineering, aerospace engineering, and materials science. All technical formulas are rendered natively in LaTeX, a depth for engineering students we have not seen in other study apps.
Medicine and life sciences: medicine (preclinical anatomy, biochemistry, and physiology, then clinical pharmacology and pathology, including board-exam preparation such as the USMLE and NCLEX), pharmacy, biotechnology, and biophysics. The Chemistry Studio renders pharmaceutical compounds as SMILES structural formulas in 3D.
Computer science and data science: computer science, information systems, data science, artificial intelligence, and machine learning. Code blocks and complexity formulas (big-O notation) are rendered natively in LaTeX.
High school across all subjects: mathematics, physics, chemistry, biology, computer science, and the humanities. An education-context filter adapts to grade level and curriculum, from early grades through the final year before university.
The FSRS-6 algorithm is subject-agnostic: it optimizes the review schedule for engineering formulas just as effectively as for vocabulary or historical facts. Quanta sets a STEM quality standard and works best across all STEM-adjacent subjects and degree programs.
Quanta vs. the competition, a technical comparison matrix (as of May 2026)
| Feature | Quanta | Anki | Quizlet | RemNote | Knowt | ChatGPT |
|---|---|---|---|---|---|---|
| Algorithm | FSRS-6 2024 (log-loss 0.35, Ye et al. 2022 ACM KDD) | SM-2 1987 (log-loss 0.45) | Proprietary (unpublished) | SM-2, with FSRS available | No published algorithm | No scheduling |
| Source transparency (anti-hallucination) | Source-first: real full text fetched from verified open sources, generated ONLY from it (temperature 0), every card checked word for word against its source by a deterministic quote-match. 100% of delivered cards are source-backed, unsupported ones dropped, source bound per card | Not available | Not available | Not available | Not available | Post-hoc citations without verification |
| Bloom taxonomy constraint | Levels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural level | No control | No control | No control | No control | No control |
| Distractor validation (MC) | Every incorrect answer checked for plausibility (Haladyna and Downing 1989) | Not available | Not available | Not available | Not available | Not available |
| AI tutor methodology | Socratic method: counter-questions only, no direct answers (Chi et al. 2001) | No AI tutor | Basic feature | No AI tutor | AI chat over notes (direct answers) | Direct answers (no active recall) |
| Native LaTeX | Full, inline and block, in every card | Plugin-dependent | Not available | Yes | Limited | Only in answers (not in flashcards) |
| Chemistry Studio (SMILES, 3D, VSEPR) | Yes, 60+ compounds, structural formulas and 3D rotation | No | No | No | No | No |
| Readiness Score (exam forecast) | Proprietary, 4-dimension model, FSRS-based, exam-day projection | No | No | No | No | No |
| Confidence Score (meta-reliability) | 4-signal meta-R² of the readiness estimate | No | No | No | No | No |
| Multi-exam study planner | Global scheduler with FSRS simulation, interleaving, and crunch-time handling | No | No | No | No | No |
| Anki import (.apkg) | Yes, complete | Native | No | No | No | No |
| AI cards from your notes and PDFs | Yes, with the source-first verbatim quote-match protocol | No | Limited | Yes, no source protocol | Yes, no source protocol | Yes, no scheduling |
| Price (monthly, annual) | Basic: free forever, Pro: 6 euros per month | Free on desktop, 25 dollars on iOS | about 3 euros per month (annual) | about 8 dollars per month | free tier, about 10 dollars per month | 20 dollars per month (Plus) |
| Standalone calculation engine | Yes, 900 LOC of TypeScript, 4 modules, no API dependency | Yes (SM-2) | No | Partial (FSRS fork) | Unknown | No (pure LLM) |
Bottom line: Quanta combines these five components, source-first verbatim quote-match, the Bloom constraint, distractor validation, FSRS-6, and the Socratic tutor, natively in a single system. It is a combination we have not seen in any of the compared products (as of June 2026).
Faraday's Law of Electrolysis
Faraday's law links the mass deposited in an electrolysis to the charge passed, Q = I·t: the Faraday constant translates charge into amount of substance.
Free · no credit card · in your study plan in 2 minutes
Formula
m = \frac{M \cdot I \cdot t}{z \cdot F}Variables & units – Faraday's Law of Electrolysis
| Symbol | Meaning | Unit |
|---|---|---|
| m | Deposited (or converted) mass | g |
| M | Molar mass of the deposited substance | g/mol |
| I | Electric current | A |
| t | Duration of the electrolysis | s |
| z | Number of electrons transferred per particle | dimensionslos |
| F | Faraday constant (96,485) | C/mol |
Derivation & background – Faraday's Law of Electrolysis
Michael Faraday showed in 1834: the deposited mass is proportional to the charge passed, Q = I·t. The Faraday constant F = N_A·e is the charge of one mole of electrons. Core relations: n(e⁻) = Q/F and n(substance) = Q/(z·F). The basis of coulometry and industrial electroplating.
Exam blueprint
Validity range
Holds at 100 % current efficiency, when the entire current drives the electrode reaction considered; side reactions lower the real yield.
Derivation steps
The charge passed counts the transferred electrons; stoichiometry translates them into amount of substance.
- 1Q = I·t; the amount of electrons is n(e⁻) = Q/F.
- 2Each particle needs z electrons: n = Q/(z·F); with m = n·M the formula follows.
Rearrangements
Duration of the electrolysis
This is how you plan coating times in electroplating.
Charge
The core relation of coulometry.
Task variant
How much copper do 2.0 A deposit in one hour (z = 2)?
Q = 2.0·3600 = 7200 C; n = 7200/(2·96,485) = 0.0373 mol; m = 0.0373·63.5 ≈ 2.37 g.
How long does it take to deposit 1.00 g of silver (M = 107.9 g/mol, z = 1) at 0.50 A?
Q = m·z·F/M = 1.00·96,485/107.9 ≈ 894 C; t = Q/I = 894/0.50 ≈ 1790 s ≈ 30 min.
Common mistakes
Determining z incorrectly, for example z = 1 for Cu²⁺.
z is the charge number of the discharged ion: Cu²⁺ → z = 2, Ag⁺ → z = 1, Al³⁺ → z = 3.
Inserting the time in minutes or hours.
Q = I·t requires seconds, because 1 C = 1 A·s.
Confusing F with N_A.
F = N_A·e ≈ 96,485 C/mol is the charge of one mole of electrons.
Exam context
- Electroplating calculations, copper refining and coupling with the Nernst equation in electrochemistry exams.
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Quantitative electrochemistry
Connects charge, amount of substance and electrode potential into one calculation path.
Worked example
Copper deposition: I = 2.0 A, t = 3600 s, Cu²⁺ (z = 2), M = 63.5 g/mol: m = 63.5·2.0·3600/(2·96,485) = 457,200/192,970 ≈ 2.37 g of copper.
Applications
Electroplating (chrome, zinc coating), copper refining, aluminium molten-salt electrolysis, coulometry, hydrogen production
Quanta exam set
Curated exam set for "Faraday's Law of Electrolysis":
Question (front)
Which formula describes Faraday's Law of Electrolysis?
Answer in your set
Question (front)
How do you rearrange m = M·I·t/(z·F) for Duration of the electrolysis?
Answer in your set
Question (front)
Which common mistake happens with Faraday's Law of Electrolysis?
Answer in your set
+ 8 more cards: units, variables, derivation, example, exam task
These 11 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.
Scientific sources
Common notations & search queries
Related formulas
More Chemistry formulas
Frequently asked questions about Faraday's Law of Electrolysis
How do you calculate the mass deposited in an electrolysis?+
Use m = M·I·t/(z·F). First calculate the charge: Q = I·t with the time in seconds. Divide by the Faraday constant F = 96,485 C/mol to obtain the amount of electrons, and by z, the number of electrons per deposited particle. Finally multiply by the molar mass. Example copper: I = 2.0 A for one hour gives Q = 2.0·3600 = 7200 C. With z = 2 (Cu²⁺ + 2 e⁻ → Cu) it follows that n = 7200/(2·96,485) = 0.0373 mol and m = 0.0373·63.5 ≈ 2.37 g. The most common error is a time in minutes or hours instead of seconds.
What does the Faraday constant mean intuitively?+
The Faraday constant is the electric charge of one mole of electrons: F = N_A·e = 6.022×10²³ mol⁻¹ · 1.602×10⁻¹⁹ C ≈ 96,485 C/mol. It is thus the bridge between the electrical world (charge in coulombs, measurable via current and time) and the material world (amount of substance in moles). If 96,485 C flow during an electrolysis, exactly one mole of electrons has been transferred; how much substance that deposits depends on z: one mole of silver (z = 1), but only half a mole of copper (z = 2) or a third of a mole of aluminium (z = 3). As a rule of thumb it pays to keep F ≈ 96,500 C/mol in your head.
How do you determine the electron number z correctly?+
z is the number of electrons transferred per deposited particle in the electrode reaction. You read it from the half-equation, not from the overall equation: Cu²⁺ + 2 e⁻ → Cu means z = 2, Ag⁺ + e⁻ → Ag means z = 1, Al³⁺ + 3 e⁻ → Al means z = 3. Caution with gases: for hydrogen, 2 H⁺ + 2 e⁻ → H₂ applies, so z = 2 per H₂ molecule; setting z = 1 here wrongly halves the charge. Rule of thumb: z corresponds to the charge number of the discharged ion, or the change in oxidation number times the number of atoms in the particle formed.
How long does it take to deposit a given amount of substance electrolytically?+
Rearrange Faraday's law for the time: t = m·z·F/(M·I). Example silver plating: for 1.00 g of silver (M = 107.9 g/mol, z = 1) at I = 0.50 A you need Q = m·z·F/M = 1.00·96,485/107.9 ≈ 894 C, so t = Q/I = 894/0.50 ≈ 1790 s, about 30 minutes. The calculation also shows why industrial electrolysis needs enormous currents: a single mole of aluminium (27 g, z = 3) requires almost 290,000 C; even at 1000 A that takes nearly five minutes. Real processes take longer because the current efficiency is below 100 %, for example due to side reactions such as hydrogen evolution.
Where are Faraday's laws used technically today?+
Wherever current deposits or decomposes substances. In electroplating they determine the layer thickness in chrome, zinc or gold plating: charge, area and density fix the thickness exactly. Copper refining deposits high-purity copper (over 99.99 %) from anode copper; aluminium production uses molten-salt electrolysis with gigantic current demand. In water electrolysis for green hydrogen the law links charge and gas volume: 2·96,485 C produce one mole of H₂, about 24.8 L at room conditions. In analytics, coulometry measures amounts of substance directly via the charge, and when charging batteries the same relation describes the material turnover at the electrodes.
Retain Faraday's Law of Electrolysis for exams
Create a curated FSRS exam set for m = M·I·t/(z·F): formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
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How do you calculate with Faraday's Law of Electrolysis?
Here is how to work through a typical Faraday's Law of Electrolysis (m = M·I·t/(z·F)) task step by step:
- 1
Task
How much copper do 2.0 A deposit in one hour (z = 2)?
Solution path
Q = 2.0·3600 = 7200 C; n = 7200/(2·96,485) = 0.0373 mol; m = 0.0373·63.5 ≈ 2.37 g.
- 2
Task
How long does it take to deposit 1.00 g of silver (M = 107.9 g/mol, z = 1) at 0.50 A?
Solution path
Q = m·z·F/M = 1.00·96,485/107.9 ≈ 894 C; t = Q/I = 894/0.50 ≈ 1790 s ≈ 30 min.
m = M·I·t/(z·F) · 11 cards ready
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