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)

FeatureQuantaAnkiQuizletRemNoteKnowtChatGPT
AlgorithmFSRS-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 availableNo published algorithmNo 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 cardNot availableNot availableNot availableNot availablePost-hoc citations without verification
Bloom taxonomy constraintLevels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural levelNo controlNo controlNo controlNo controlNo control
Distractor validation (MC)Every incorrect answer checked for plausibility (Haladyna and Downing 1989)Not availableNot availableNot availableNot availableNot available
AI tutor methodologySocratic method: counter-questions only, no direct answers (Chi et al. 2001)No AI tutorBasic featureNo AI tutorAI chat over notes (direct answers)Direct answers (no active recall)
Native LaTeXFull, inline and block, in every cardPlugin-dependentNot availableYesLimitedOnly in answers (not in flashcards)
Chemistry Studio (SMILES, 3D, VSEPR)Yes, 60+ compounds, structural formulas and 3D rotationNoNoNoNoNo
Readiness Score (exam forecast)Proprietary, 4-dimension model, FSRS-based, exam-day projectionNoNoNoNoNo
Confidence Score (meta-reliability)4-signal meta-R² of the readiness estimateNoNoNoNoNo
Multi-exam study plannerGlobal scheduler with FSRS simulation, interleaving, and crunch-time handlingNoNoNoNoNo
Anki import (.apkg)Yes, completeNativeNoNoNoNo
AI cards from your notes and PDFsYes, with the source-first verbatim quote-match protocolNoLimitedYes, no source protocolYes, no source protocolYes, no scheduling
Price (monthly, annual)Basic: free forever, Pro: 6 euros per monthFree on desktop, 25 dollars on iOSabout 3 euros per month (annual)about 8 dollars per monthfree tier, about 10 dollars per month20 dollars per month (Plus)
Standalone calculation engineYes, 900 LOC of TypeScript, 4 modules, no API dependencyYes (SM-2)NoPartial (FSRS fork)UnknownNo (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).

Physics · Mechanics

Buoyant Force (Archimedes)

The buoyant force after Archimedes equals the weight of the displaced fluid; it decides floating, hovering and sinking.

BasicExam-relevant

Free · no credit card · in your study plan in 2 minutes

Formula

F_A = ρ·V·g
LaTeX: F_A = \rho_{Fl} \cdot V \cdot g
F_A in N · ρ_Fl in kg/m³ · V in m³ · g = 9.81 m/s²

Variables & units – Buoyant Force (Archimedes)

SymbolMeaningUnit
F_ABuoyant force (directed upward)N
ρ_FlDensity of the fluid (water: 1000 kg/m³)kg/m³
VDisplaced volume (submerged part)
gGravitational acceleration (9.81 m/s²)m/s²

Derivation & background – Buoyant Force (Archimedes)

Archimedes of Syracuse formulated the principle around 250 BC. Buoyancy arises because pressure in a fluid increases with depth: the underside of a body experiences a larger pressure than the top, and the difference equals exactly the weight of the displaced fluid. The density comparison decides: ρ_body < ρ_fluid floats, equal hovers, larger sinks. A floating body submerges just far enough that F_A = F_G.

Exam blueprint

Validity range

Applies in fluids at rest (liquids and gases) for fully wetted bodies. V is only the submerged volume, ρ_Fl the density of the fluid, not of the body.

Derivation steps

Pressure increases with depth, so the fluid pushes harder from below than from above.

  1. 1The bottom of a cube (area A, height Δh) experiences a pressure larger by ρ·g·Δh.
  2. 2The force difference is F_A = ρ·g·Δh·A = ρ·V·g, the weight of the displaced fluid.

Rearrangements

Displaced volume

V = \frac{F_A}{\rho_{Fl} \cdot g}

This yields the volume of irregular bodies from a measured buoyant force.

Fluid density

\rho_{Fl} = \frac{F_A}{V \cdot g}

The principle of the hydrometer for density measurement.

Task variant

A wooden block (ρ = 600 kg/m³) floats in water. What fraction of its volume is submerged?

Floating condition F_A = F_G: ρ_W·V_sub·g = ρ_wood·V·g. So V_sub/V = 600/1000 = 0.6, meaning 60 % is submerged.

An aluminium body (m = 2.7 kg, ρ = 2700 kg/m³) hangs under water from a scale. What does it read?

V = m/ρ = 0.001 m³, F_A = 1000 × 0.001 × 9.81 = 9.81 N. Apparent weight: 26.49 − 9.81 = 16.68 N, about 1.7 kg.

Common mistakes

Using the density of the body instead of the fluid.

The formula always takes the fluid density; the body density only decides floating or sinking.

Using the total volume of a floating body instead of the submerged volume.

Only the submerged part displaces fluid and creates buoyancy.

Substituting the volume in litres directly.

Convert first: 1 L = 0.001 m³, otherwise the result is off by a factor of 1000.

Forgetting buoyancy in gases.

Air also produces buoyancy, which lifts hot-air balloons and biases precision weighing.

Exam context

  • Classics are float/hover/sink decisions via density comparison, apparent weight under water and the submerged volume fraction of floating bodies.

These mistakes cost points in real exams. The set drills them until they stick.

Formula cluster

Hydrostatics

Belongs with pressure and density, the two basic quantities of fluids at rest.

Worked example

A body displaces V = 2 L = 0.002 m³ of water (ρ = 1000 kg/m³): F_A = 1000 × 0.002 × 9.81 = 19.62 N. A 5 kg stone (F_G = 49.05 N) apparently weighs only 29.43 N under water.

Applications

Shipbuilding (displacement and load line), submarines and ballast tanks, hot-air balloons, hydrometers for density measurement, icebergs

Quanta exam set

Curated exam set for "Buoyant Force (Archimedes)":

Question (front)

Which formula describes Buoyant Force (Archimedes)?

Answer in your set

Question (front)

How do you rearrange F_A = ρ·V·g for Displaced volume?

Answer in your set

Question (front)

Which common mistake happens with Buoyant Force (Archimedes)?

Answer in your set

+ 7 more cards: units, variables, derivation, example, exam task

These 10 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.

Scientific sources

Common notations & search queries

FA=rho*V*gAuftrieb FormelArchimedisches Prinzipbuoyancy formulaAuftriebskraft berechnenschwimmen schweben sinkenverdrängtes VolumenAuftrieb Wasser

Related formulas

More Physics formulas

Frequently asked questions about Buoyant Force (Archimedes)

How do you calculate the buoyant force in water?+

Multiply the density of water (1000 kg/m³) by the submerged volume in m³ and by g = 9.81 m/s². A fully submerged body of 2 litres displaces V = 0.002 m³, so F_A = 1000 × 0.002 × 9.81 = 19.62 N. That equals the weight of about 2 kg of water, completely independent of what the body is made of or how heavy it is. Two points matter: convert the volume from litres to cubic metres (1 L = 0.001 m³), and for partially submerged bodies count only the part below the water surface.

Why does a steel ship float although steel is denser than water?+

What counts is not the density of the material but the average density of the whole body including the enclosed air. A hull is hollow inside: steel walls plus huge air spaces together give an average density well below 1000 kg/m³. The ship therefore sinks in only until the displaced water weighs as much as the whole ship; then F_A = F_G and it floats stably. A solid steel block, by contrast, has ρ ≈ 7850 kg/m³ and sinks. If the hull floods, the air spaces vanish, the average density rises above that of water, and the ship goes down.

How do you rearrange the buoyancy formula for the volume?+

Divide the buoyant force by density and gravitational acceleration: V = F_A/(ρ_Fl·g). This measures the volume of irregular bodies elegantly: weigh the body once in air and once fully submerged. The difference between the two readings is the buoyant force. If the scale shows 49.05 N in air and 29.43 N under water, then F_A = 19.62 N and V = 19.62/(1000 × 9.81) = 0.002 m³ = 2 L. According to tradition, Archimedes checked King Hiero golden crown with exactly this idea: weight and volume give the density, and the density reveals the material.

When does a body float, hover or sink?+

Compare the average density of the body with the density of the fluid. If ρ_body is smaller, buoyancy wins when fully submerged, the body rises and finally floats with only part of it submerged. If both densities are equal, weight and buoyancy cancel exactly and the body hovers at any depth (this is how submarines work with their ballast tanks). If ρ_body is larger, weight wins and it sinks. For a floating body the submerged fraction can be read off directly: V_sub/V = ρ_body/ρ_fluid. Ice at 917 kg/m³ therefore floats about 92 % submerged, only the tip of the iceberg shows.

Does the Archimedes principle also apply in air and other gases?+

Yes, the formula F_A = ρ·V·g holds in any fluid, including gases. However, the density of air, about 1.2 kg/m³, is roughly 800 times smaller than that of water, so we hardly notice air buoyancy in everyday life. A person with about 75 L of body volume still experiences about 0.9 N of buoyancy in air, equivalent to roughly 90 grams. Hot-air balloons use the principle deliberately: hot air inside the balloon is less dense than the cold outside air, the displaced cold air weighs more than the filling, and the difference carries basket and envelope. Precision weighing must also correct for air buoyancy.

Retain Buoyant Force (Archimedes) for exams

Create a curated FSRS exam set for F_A = ρ·V·g: formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.

Free · curated formula set · LaTeX · FSRS spaced repetition

How do you calculate with Buoyant Force (Archimedes)?

Here is how to work through a typical Buoyant Force (Archimedes) (F_A = ρ·V·g) task step by step:

  1. 1

    Task

    A wooden block (ρ = 600 kg/m³) floats in water. What fraction of its volume is submerged?

    Solution path

    Floating condition F_A = F_G: ρ_W·V_sub·g = ρ_wood·V·g. So V_sub/V = 600/1000 = 0.6, meaning 60 % is submerged.

  2. 2

    Task

    An aluminium body (m = 2.7 kg, ρ = 2700 kg/m³) hangs under water from a scale. What does it read?

    Solution path

    V = m/ρ = 0.001 m³, F_A = 1000 × 0.001 × 9.81 = 9.81 N. Apparent weight: 26.49 − 9.81 = 16.68 N, about 1.7 kg.

F_A = ρ·V·g · 10 cards ready

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